“…The principle, development history, and research status of wavelet transform theory and wavelet image coding technology are systematically discussed. Sadkhan [ 20 ] used the MATLAB wavelet toolbox function to realize the simulation of image compression based on wavelet transform. The researcher expounds the basic principle and development history of artificial neural network in detail, analyzes the network structure and working principle of improving wavelet neural network, and proposes improvement measures for the shortcomings of improving wavelet network.…”
Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved.
“…The principle, development history, and research status of wavelet transform theory and wavelet image coding technology are systematically discussed. Sadkhan [ 20 ] used the MATLAB wavelet toolbox function to realize the simulation of image compression based on wavelet transform. The researcher expounds the basic principle and development history of artificial neural network in detail, analyzes the network structure and working principle of improving wavelet neural network, and proposes improvement measures for the shortcomings of improving wavelet network.…”
Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved.
“…As a result, it is common to feed QKD-derived key material to traditional computationally secure cryptographic algorithms like Advanced Encryption Standard (AES). This broadens the applicability of QKD technology across many critical infrastructures (5G [24] , [25] , [26] , [27] , 6G [28] , [29] , SCADA [30] , and smart grids [31] , [32] , [33] ) and allows it to accommodate users with varying security requirements. Figure 2 Distribution of the cryptographic key from node A to node D over the QKD network in a hop-by-hop manner.…”
“…In this recent age, for extracting features different techniques have been employed, for example, texture features, Gabor features, co-occurrence matrix, and more [10]. Indeed, Gray Level Cooccurrence Matrix (GLCM) and Local Binary Pattern (LBP) are the most common and robust techniques of texture image description that are widely utilized for analyzing image applications [11,12,24,25,26]. The main objectives of this paper are; LBP, FD, and GLCM are the most common as well as the most efficient techniques to produce high-level features that are used in the literature.…”
Coronavirus (COVID-19) is a new contagiousdisease reasoned by a new virus that is widely spread over the world, this virus never has been identified in humans before. Respiratory disease can be affected by this virus such as flu with several symptoms, for example, fever, headache, cough, and pneumonia. COVID-19 presence in humans can be tested through blood samples or sputum while the result can be obtained in days. Further, biomedical image analysis assists in showing signs of pneumonia in a patient. Therefore, this paper aims to provide a fully automatic COVID-19 identification system by proposing a new fusion scheme of texture features for CT scan images. This paper presents a fusion scheme based on a machine learning system using three significant texture features, namely, Local Binary Pattern (LBP), Fractal Dimension (FD), and Grey Level Co-occurrence Matrices (GLCM). In experimental results, to demonstrate the efficiency of the proposed scheme we have collected 300 CT scan images from a publicly available database. The experimental result shows the performance of LBP, FD, and GLCM obtained an accuracy of 89.87%, 87.84%, and 90.98%, respectively while the proposed scheme yields better results by achieving 96.91% accuracy.
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