Hybrid FGWO Based FLCs Modeling for Performance Enhancement in Wireless Body Area Networks

Banu, S Sindhuja; Baskaran, K.

doi:10.1007/s11277-018-5626-4

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…We assume that α, β, and δ know more about the location of the prey. Therefore, the ω wolves must update their positions according to the three best positions obtained so far (Banu & Baskaran, 2018). The characteristics of the wolves can be expressed numerically as…”

Section: Gwo Optimization Algorithmmentioning

confidence: 99%

Improved PCNN Polarization Image Denoising Method Based on Grey Wolf Algorithm and Non-Subsampled Contourlet Transform

Li,

Sun,

Feng

2023

JCCE

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In this article, an adaptive pulse-coupled neural network (PCNN) polarization image denoising method based on Grey Wolf Optimization (GWO) and Non-Subsampled Contourlet Transform(NSCT) is proposed. Different from the traditional PCNN denoising method, the captured polarization image was firstly devised by the NSCT and enforced band-decomposition to denoised by PCNN. The evaluable index of the image was used for quantitative analysis. Then, GWO is used to update PCNN inherent voltage constant and attenuation time constant and neurons connected intensity factor three model parameters, after looking for multiple optimal solutions, and then to polarized image denoising to achieve the best effect. This method not only avoids the image edge blurring caused by the traditional image denoising method, but also solves the problem that the parameters of the PCNN are difficult to accurately estimate. Hence, it is more suitable for polarization images containing noise. The experiment and the quantitative analysis of image evaluation indices showed that NSCT-GWO-PCNN effectively suppresses the noise in polarization image by reducing salt-and-pepper noise while protecting edges.

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Section: Gwo Optimization Algorithmmentioning

confidence: 99%

Improved PCNN Polarization Image Denoising Method Based on Grey Wolf Algorithm and Non-Subsampled Contourlet Transform

Li,

Sun,

Feng

2023

JCCE

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“…where t indicates the current iteration, We assume that α, β and δ all have better knowledge about the potential location of the prey. Therefore, the other grey wolves represented by ω are obliged to update their positions based on the best three positions obtained to date [44]. The grey hunting characteristic can then be presented numerically as follows:…”

Section: Gwo Algorithmmentioning

confidence: 99%

Salt and Pepper Noise Removal for Image Using Adaptive Pulse-Coupled Neural Network Optimized by Grey Wolf Optimization and Bidimensional Empirical Mode Decomposition

Min

Wang

et al. 2018

Applied Sciences

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Aimed at the problem of poor noise reduction effect and parameter uncertainty of pulse-coupled neural network (PCNN), a hybrid image denoising method, using an adaptive PCNN that has been optimized by grey wolf optimization (GWO) and bidimensional empirical mode decomposition (BEMD), is presented. The BEMD is used to decompose the original image into multilayer image components. After a GWO is run to complete PCNN parameter optimization, an adaptive PCNN filter method is used to remediate the polluted noise points that correspond to the different image components, from which a reconstruction of the denoised image components can then be obtained. From an analysis of the image denoising results, the main advantages of the proposed method are as follows: (i) the method effectively solves the deficiencies that arise from the critical PCNN parameter determination issue; (ii) the method effectively overcomes the problem of high-intensity noise effects by providing a more targeted and efficient noise reduction process; (iii) when using this method, the noise points are isolated, and the original pixel points are restored well, which can lead to preservation of image detail information. When compared with traditional image denoising process algorithms, the proposed method can yield a better noise suppression effect, based on indicators including analysis of mutual information (MI), structural similarity (SSIM), the peak signal-to-noise ratio (PSNR) and the standard deviation (STD). The feasibility and applicability of the proposed denoising algorithm are also demonstrated experimentally.

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E-MHMS: enhanced MAC-based secure delay-aware healthcare monitoring system in WBAN

Sharmila

Jaisankar

2020

Cluster Comput

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Hybrid FGWO Based FLCs Modeling for Performance Enhancement in Wireless Body Area Networks

Cited by 6 publications

References 30 publications

Improved PCNN Polarization Image Denoising Method Based on Grey Wolf Algorithm and Non-Subsampled Contourlet Transform

Improved PCNN Polarization Image Denoising Method Based on Grey Wolf Algorithm and Non-Subsampled Contourlet Transform

Salt and Pepper Noise Removal for Image Using Adaptive Pulse-Coupled Neural Network Optimized by Grey Wolf Optimization and Bidimensional Empirical Mode Decomposition

E-MHMS: enhanced MAC-based secure delay-aware healthcare monitoring system in WBAN

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