Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C<sup>4</sup>D Equipment

Tong, Yaonan; Li, Jingui; Xu, Yaohui; Cao, Lichen

doi:10.1155/2020/6481317

Cited by 7 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The equation above clearly demonstrates that the adjustment of the parameter 'm' enables the threshold function to dynamically transition between the hard threshold function, as described in Ref. [ 26 ], and the soft threshold function, as discussed in Ref. [ 27 ].…”

Section: Sea Clutter Pre-processing Algorithmsmentioning

confidence: 83%

Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM

Shang,

Zhu,

Liu

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Sea Clutter Pre-processing Algorithmsmentioning

confidence: 83%

Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM

Shang,

Zhu,

Liu

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Relevant research institutions in Europe and America first proposed to filter the noise signal in the speech signal by using wavelet decomposition and corresponding reconstruction technology based on the multiresolution theory. This method is pioneering [19]. Based on the above, relevant research institutions have improved this technology.…”

Section: Correlation Analysis: Research Status Of English Speech Translation Recognition Algorithm Based On Wavelet Transformmentioning

confidence: 99%

Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method

Zhuo

2021

Advances in Mathematical Physics

View full text Add to dashboard Cite

The signal corresponding to English speech contains a lot of redundant information and environmental interference information, which will produce a lot of distortion in the process of English speech translation signal recognition. Based on this, a large number of studies focus on encoding and processing English speech, so as to achieve high-precision speech recognition. The traditional wavelet denoising algorithm plays an obvious role in the recognition of English speech translation signals, which mainly depends on the excellent local time-frequency domain characteristics of the wavelet signal algorithm, but the traditional wavelet signal algorithm is still difficult to select the recognition threshold, and the recognition accuracy is easy to be affected. Based on this, this paper will improve the traditional wavelet denoising algorithm, abandon the single-threshold judgment of the original traditional algorithm, innovatively adopt the combination of soft threshold and hard threshold, further solve the distortion problem of the denoising algorithm in the process of English speech translation signal recognition, improve the signal-to-noise ratio of English speech recognition, and further reduce the root mean square error of the signal. Good noise reduction effect is realized, and the accuracy of speech recognition is improved. In the experiment, the algorithm is compared with the traditional algorithm based on MATLAB simulation software. The simulation results are consistent with the actual theoretical results. At the same time, the algorithm proposed in this paper has obvious advantages in the recognition accuracy of English speech translation signals, which reflects the superiority and practical value of the algorithm.

show abstract

“…While non-parametric thresholding functions in Refs. [18,19] and exhibits improved denoising effects compared to traditional methods, they lack sufficient adaptive capabilities. Parametric thresholding functions, on the other hand, are proposed in Refs.…”

Section: Introductionmentioning

confidence: 99%

Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

Zhu,

Fu,

et al. 2023

Electronics

View full text Add to dashboard Cite

The wavelet threshold functions are widely used in oil chromatography denoising because high-quality signals are the basis for Dissolved Gas Analysis (DGA), which determines the accuracy of transformer fault monitoring. However, there are certain limitations of the wavelet threshold functions, such as the Pseudo-Gibbs phenomenon and improper threshold selection. To this purpose, a modified genetic particle swarm optimization-based improved threshold function denoising method (MGPSO-ITF) is proposed. Specifically, the method constructs a new parametric threshold function that possesses high-order derivability and a small constant deviation. To obtain optimal values for the tunable parameters, MGPSO is employed, which outperforms other methods in identifying the optimum and achieving fast convergence. The simulation results demonstrate that the enhanced thresholding function yields a higher Signal-to-Noise Ratio (SNR), higher Noise Suppression Ratio (NSR), and smaller Root Mean Square Error (RMSE) compared to prior methods. Specifically, for the originally relatively smooth signal, MGPSO-ITF does not over-correct it to cause distortion. Furthermore, experiments on measured signals illustrate that the MGPSO-ITF is highly effective at denoising and preserving the original signal properties. Particularly in cases where peak deformation is prominent, the algorithm outperforms both hard and soft thresholding methods, achieving a reduction of 2.934% and 1.029% in peak area error, respectively.

show abstract

Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C⁴D Equipment

Cited by 7 publications

References 22 publications

Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM

Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM

Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method

Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

Contact Info

Product

Resources

About

Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C4D Equipment

Cited by 7 publications

References 22 publications

Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM

Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM

Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method

Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

Contact Info

Product

Resources

About

Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C⁴D Equipment