Filter group delays equalization for 2D discrete wavelet transform applications

Bahoura, Mohammed; Ezzaidi, Hassan; Méthot, Jean-François

doi:10.1016/j.eswa.2022.116954

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…In signal separation applications, DWT can be used to separate features from time series and create an appropriate classification model [44]. It is a powerful and widely used signal processing tool for decomposing time-varying signals into frequency signals using scaling factors [45][46][47].…”

Section: Proposed Methodsmentioning

confidence: 99%

Blind Source Separation with Strength Pareto Evolutionary Algorithm 2 (SPEA2) Using Discrete Wavelet Transform

Celik,

Karaboga

2023

Electronics

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This paper presents a new method for separating the mixed audio signals of simultaneous speakers using Blind Source Separation (BSS). The separation of mixed signals is an important issue today. In order to obtain more efficient and superior source estimation performance, a new algorithm that solves the BSS problem with Multi-Objective Optimization (MOO) methods was developed in this study. In this direction, we tested the application of two methods. Firstly, the Discrete Wavelet Transform (DWT) was used to eliminate the limited aspects of the traditional methods used in BSS and the small coefficients in the signals. Afterwards, the BSS process was optimized with the multi-purpose Strength Pareto Evolutionary Algorithm 2 (SPEA2). Secondly, the Minkowski distance method was proposed for distance measurement by using density information in the discrimination of individuals with raw fitness values for the concept of Pareto dominance. With this proposed method, the originals (original source signals) were estimated by separating the randomly mixed male and two female speech signals. Simulation and experimental results proved that the efficiency and performance of the proposed method can effectively solve BSS problems. In addition, the Pareto front approximation performance of this method also confirmed that it is superior in the Inverted Generational Distance (IGD) indicator.

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Section: Proposed Methodsmentioning

confidence: 99%

Blind Source Separation with Strength Pareto Evolutionary Algorithm 2 (SPEA2) Using Discrete Wavelet Transform

Celik,

Karaboga

2023

Electronics

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“…As a typical 2D EWT method Littlewood-Paley method aroused wide concern. The conventional 2D Littlewood-Paley wavelet transform corresponds to the filter image of a 2D wavelet with circular supports (centered at the origin) defined in the Fourier domain [34]. The inner and outer radii of these supports are fixed in the binary decomposition plane of the Fourier domain, i.e., the scale factor.…”

Section: Two-dimensional Empirical Wavelet Transformsmentioning

confidence: 99%

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Man,

Zhou,

Sun

et al. 2024

Surf. Topogr.: Metrol. Prop.

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Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in-situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is proposed in this research. By evaluating the texture similarity intelligently between the testing image and the reference image, the surface roughness of the testing image can be acquired. Firstly, with a proposed adaptive texture extraction method, the texture feature of an image can be extracted even under a complex background. Secondly, by establishing the graph structure of the texture grayscale features, the similarity between different images is evaluated. Finally, by establishing a sparrow-optimized support vector machine regression method, the mapping relationship between the similarity and the surface roughness can be acquired. The experimental results indicate that the proposed method for intelligent evaluation of roughness has superior prediction performance (the average relative prediction error of Ra and Rz are 8.8156% and 8.0571%, respectively). Therefore, this work provides a useful tool for non-contact detection of workpiece surface roughness.

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CerCan·Net: Cervical cancer classification model via multi-layer feature ensembles of lightweight CNNs and transfer learning

Attallah

2023

Expert Systems with Applications

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Filter group delays equalization for 2D discrete wavelet transform applications

Cited by 4 publications

References 25 publications

Blind Source Separation with Strength Pareto Evolutionary Algorithm 2 (SPEA2) Using Discrete Wavelet Transform

Blind Source Separation with Strength Pareto Evolutionary Algorithm 2 (SPEA2) Using Discrete Wavelet Transform

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

CerCan·Net: Cervical cancer classification model via multi-layer feature ensembles of lightweight CNNs and transfer learning

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