A wavelet filter comparison on multiple datasets for signal compression and denoising

Gnutti, Alessandro; Guerrini, Fabrizio; Adami, Nicola; Migliorati, Pierangelo; Leonardi, Riccardo

doi:10.1007/s11045-020-00753-w

Cited by 10 publications

(3 citation statements)

References 48 publications

(46 reference statements)

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“…DWT is implemented using a two-band perfect reconstruction flter bank. In many applications, such as signal fltering [31,32], compactly supported wavelets are required to realize perfect signal reconstruction. However, a real orthogonal symmetric wavelet basis, such as the Haar wavelet, is not continuous and the flter is of the order 1 only, which is not adequate for practical applications.…”

Section: Symmetric Boundary Extensionmentioning

confidence: 99%

Discrete Wavelet Transform-based Approach of Real-time Wave Filtering for Dynamic Positioning of Marine Vessels

Liu¹,

Zhang

Gu³

et al. 2022

Mathematical Problems in Engineering

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The ongoing development of deep-sea resources has contributed toward the widespread use of dynamic positioning (DP) systems that can operate in arbitrary sea areas without the limitation of operating water depth compared to mooring systems. The second-order slowly varying forces can be compensated by DP feedback control. Wave filtering techniques are crucial for designing the DP controller, which can improve the positioning performance and service of multiple actuators. The objective of wave filtering is to separate the motion induced by the first-order high-frequency oscillatory waves from the motion caused by the second-order low-frequency (LF) slowly varying disturbances. This paper proposes a novel real-time wave filtering approach for DP systems on the basis of the discrete wavelet transform without relying on the previous knowledge of dynamics of the vessel. Meanwhile, to suppress the end effects of wavelet filtering and achieve real-time application, symmetric boundary extension and the sliding window method are investigated in order to capture the slow time-varying LF motion of DP vessels. The proposed real-time wavelet filtering approach can significantly improve the quality of the separated components by selecting the optimal decomposition level, wavelet function parameters, and threshold denoising method. The real-time and filtering performance of the proposed approach is verified using a scaled model of an offshore supply vessel as well as full-scale experiments on the EXPLOERE I scientific investigation vessel under the action of winds, currents, and waves. The experimental results confirm that the proposed wave filtering algorithm is efficient and that the LF component waveform can be effectively recovered after wavelet filtering.

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Section: Symmetric Boundary Extensionmentioning

confidence: 99%

Discrete Wavelet Transform-based Approach of Real-time Wave Filtering for Dynamic Positioning of Marine Vessels

Liu¹,

Zhang

Gu³

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…There are too many contributions to be cited here. However, one can cite a recent article dealing with orthogonal and biorthogonal wavelets in context of filter banks implementations, namely [35]. Nowadays, wavelets are employed in many fields of science and technology.…”

Section: Introductionmentioning

confidence: 99%

John von Neumann’s Time-Frequency Orthogonal Transforms

Ștefănoiu

Culita

2023

Mathematics

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John von Neumann (JvN) was one of the greatest scientists and minds of the 20th century. His research encompassed a large variety of topics (especially from mathematics), and the results he obtained essentially contributed to the progress of science and technology. Within this article, one function that JvN defined long time ago, namely the cardinal sinus (sinc), was employed to define transforms to be applied on 1D signals, either in continuous or discrete time. The main characteristics of JvN Transforms (JvNTs) are founded on a theory described at length in the article. Two properties are of particular interest: orthogonality and invertibility. Both are important in the context of data compression. After building the theoretical foundation of JvNTs, the corresponding numerical algorithms were designed, implemented and tested on artificial and real signals. The last part of the article is devoted to simulations with such algorithms by using 1D signals. An extensive analysis on JvNTs effectiveness is performed as well, based on simulation results. In conclusion, JvNTs prove to be useful tools in signal processing.

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“…When the frequency of the noise signal is close to that of the target signal, it is difficult to filter out using conventional filtering methods. Hence, some scholars have proposed wavelet filtering methods that think noise signals mainly contain high-frequency components (Daubechies, 2015;Patidar et al, 2015;Thirumala et al, 2015;DomínguezNavarro José et al, 2021;Gnutti et al, 2021;Zhao, 2021). Firstly, the collected signal was decomposed into the highfrequency part and low-frequency part, then the high-frequency signal was thresholded using the threshold method, and finally the processed signals were combined.…”

Section: Introductionmentioning

confidence: 99%

Study on Optimization of Infrasound Filtering Method for Coal Sample Failure under Load

et al. 2022

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Effective filtering of the infrasound signal generated by coal samples is the basis for realizing the prediction of the infrasound of coal sample damage. Based on the infrasonic signal test of the coal samples during the loading process, a simulation method was used to construct a mixed signal containing noise signals and infrasound signals. Three methods are used to filter the mixed signal, including wavelet filtering, EMD filtering, and EMD-wavelet joint filtering. The filtering effect was compared by correlation coefficient, signal-to-noise ratio, and frequency domain waveform graph. The comparison results showed that the EMD-wavelet joint filtering method had the highest correlation coefficient and signal-to-noise ratio after noise filtering, and the noise signal in the frequency domain waveform diagram was the most thorough. It provides a new method for filtering infrasound signals in the process of coal sample loading, which is greatly significant for improving the accuracy of infrasound prediction of coal sample damage.

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A wavelet filter comparison on multiple datasets for signal compression and denoising

Cited by 10 publications

References 48 publications

Discrete Wavelet Transform-based Approach of Real-time Wave Filtering for Dynamic Positioning of Marine Vessels

Discrete Wavelet Transform-based Approach of Real-time Wave Filtering for Dynamic Positioning of Marine Vessels

John von Neumann’s Time-Frequency Orthogonal Transforms

Study on Optimization of Infrasound Filtering Method for Coal Sample Failure under Load

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