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Article
Publication date: 26 November 2021

K. Upendra Raju and N. Amutha Prabha

Steganography is a data hiding technique used in the data security. while transmission of data through channel, no guarantee that the data is transmitted safely or not. Variety of…

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Abstract

Purpose

Steganography is a data hiding technique used in the data security. while transmission of data through channel, no guarantee that the data is transmitted safely or not. Variety of data security techniques exists such as patch work, low bit rate data hiding, lossy compression etc. This paper aims to increase the security and robustness.

Design/methodology/approach

This paper describes, an approach for multiple images steganography that is oriented on the combination of lifting wavelet transform (LWT) and discrete cosine transform (DCT). Here, we have one cover image and two secret images. The cover image is applied with one of the different noises like Gaussian, Salt & Pepper, Poisson, and speckle noises and converted into different color spaces of YCbCr, HSV, and Lab.

Findings

Due to the vast development of Internet access and multimedia technology, it becomes very simple to hack and trace secret information. Using this steganography process in reversible data hiding (RDH) helps to prevent secret information.

Originality/value

We can divide the color space converted image into four sub-bands of images by using lifting wavelet transform. By selecting lower bands, the discrete cosine transform is computed for hiding two secret images into the cover image and again one of the transformed secret images is converted by using Arnold transform to get the encrypted/embedded/encoded image. To extract the Stego image, we can apply the revertible operation. For comparing the results, we can calculate PSNR, SSIM, and MSE values by applying the same process for all color spaces of YCbCr, HSV, and Lab. The experimental results give better performance when compared to all other spaces.

Details

International Journal of Intelligent Unmanned Systems, vol. 11 no. 1
Type: Research Article
ISSN: 2049-6427

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Article
Publication date: 27 July 2021

Papangkorn Pidchayathanakorn and Siriporn Supratid

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations…

110

Abstract

Purpose

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Design/methodology/approach

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Findings

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

Research limitations/implications

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

Practical implications

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

Originality/value

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

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Article
Publication date: 23 September 2022

Hamed Al-sorory, Mohammed S. Gumaan and Rizk Mostafa Shalaby

This paper aims to summarise the effects of ZnO nanoparticles (0.1, 0.3, 0.5, 0.7 and 1.0 Wt.%) on the structure, mechanical, electrical and thermal stability of Sn–3.5Ag–0.5Cu…

126

Abstract

Purpose

This paper aims to summarise the effects of ZnO nanoparticles (0.1, 0.3, 0.5, 0.7 and 1.0 Wt.%) on the structure, mechanical, electrical and thermal stability of Sn–3.5Ag–0.5Cu (SAC355) solder alloys for high-performance applications.

Design/methodology/approach

The phase identification and morphology of the solders were studied using X-ray diffraction and scanning electron microscopy. Thermal parameters were investigated using differential scanning calorimetry. The elastic parameters such as Young's modulus (E) and internal friction (Q−1) were investigated using the dynamic resonance technique, whereas the Vickers hardness (Hv) and creep indentation (n) were examined using a Vickers microhardness tester.

Findings

Microstructural analysis revealed that ZnO nanoparticles (NPs) were distributed uniformly throughout the Sn matrix. Furthermore, addition of 0.1, 0.3 and 0.7 Wt.% of ZnO NPs to the eutectic (SAC355) prevented crystallite size reduction, which increased the strength of the solder alloy. Mechanical parameters such as Young's modulus improved significantly at 0.1, 0.3 and 0.7 Wt.% ZnO NP contents compared to the ZnO-free alloy. This variation can be understood by considering the plastic deformation. The Vickers hardness value (Hv) increased to its maximum as the ZnO NP content increased to 0.5. A stress exponent value (n) of approximately two in most composite solder alloys suggested that grain boundary sliding was the dominant mechanism in this system. The electrical resistance (ρ) increased its maximum value at 0.5 Wt.% ZnO NPs content. The addition of ZnO NPs to plain (SAC355) solder alloys increased the melting temperature (Tm) by a few degrees.

Originality/value

Development of eutectic (SAC355) lead-free solder doped with ZnO NPs use for electronic packaging.

Details

Soldering & Surface Mount Technology, vol. 35 no. 3
Type: Research Article
ISSN: 0954-0911

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