scholarly journals Image Hiding Using QR Factorization And Discrete Wavelet Transform Techniques

2021 ◽  
Vol 6 (2) ◽  
pp. 72-81
Author(s):  
Reham Ahmed El-Shahed ◽  
◽  
Maryam Al-Berry ◽  
Hala Ebied ◽  
Howida A. Shedeed ◽  
...  

Steganography is one of the most important tools in the data security field as there is a huge amount of data transferred each moment over the internet. Hiding secret messages in an image has been widely used because the images are mostly used in social media applications. The proposed algorithm is a simple algorithm for hiding an image in another image. The proposed technique uses QR factorization to conceal the secret image. The technique successfully hid a gray and color image in another one and the performance of the algorithm was measured by PSNR, SSIM and NCC. The PSNR for the cover image was in the range of 41 to 51 dB. DWT was added to increase the security of the method and this enhanced technique increased the cover PSNR to 48 t0 56 dB. The SSIM is 100% and the NCC is 1 for both implementations. Which improves that the imperceptibility of the algorithm is very high. The comparative analysis showed that the performance of the algorithm is better than other state-of-the-art algorithms

Healthcare system holds a huge amount of data due to the number of tests taken by the patients and are maintained in physical files or digital files in form of records. At the end of the treatment, a discharge summary of the files will be sent along with the patients. All the records of the patients are maintained in the hospital by MRD (Medico Record Department). Maintanence of records are of very high cost and data security, integrity should be maintained. When patients migrate from one hospital to another, they ought to provide their past records to the doctor for diagnosis. There is a very high probability for the patients to miss their records after many years. A social network based system can be developed in which the patient’s data is stored in cloud and can be shared among the healthcare centres. Each hospital can send requests regarding a patients’s past records to other hospital and records can be retrieved. This helps doctors to have a greater incite on the reports, and repeated tests are avoided for the search of the missing information. As medical records are very sensitive, security and privacy concerns are high. To secure data, blockchain is used in which the data are stored in off-chain and their hashes are stored on-chain. The data in off-chain are encrypted using Advanced Encryption Standard(AES).


With the advancements in the field of automation in the industries the use of machines is very high and if the machine which require some rotatory action for the load, the Induction motor comes in to play because of the advantages such as robustness, low maintenance, low cost etc. But with the increase in the dependency over the motors it becomes highly recommended to have machines with reliability because break in the work can lead to huge amount of loss. In order to increase the reliability of the motors predictive maintenance comes into play which requires fault classification or detection which is easily and accurately possible using the Machine Learning algorithms. With the requirements of the present scenario for predictive maintenance, this paper presents the fault classification of induction motor using Support Vector Machine SVM) and K- Nearest Neighbour (KNN) technique of classification. Here in this paper the bearing fault (BF) and broken rotor bar (BRB) fault is considered. The results collected are on the basis of validation and Principle Component Analysis (PCA) technique. And it is found that the SVM technique is better than the KNN for fault classification of Induction motor.


Information security is an important task on multimedia and communication world. During storing and sharing maintaining a strategic distance from the outsider access of information is the difficult one. There are many encryption algorithms that can provide data security. In this paper two of the encryption algorithms namely AES and RSA are implemented for color images. AES (Advanced Encryption Standard) is a symmetric key block cipher published in December 2001 by NSIT (National Institute of Standards and Technology). RSA (Rivest-Shamir-Adleman) is an asymmetric key block cipher. It uses two separate keys, one for encryption called the public key and other for decryption called the private key. Both the implementation and analysis are done in Matlab. The quality and security level of both the algorithms is analysed based on various criteria such as Histogram analysis, Correlation analysis, Entropy analysis, NPCR (Number of Pixel Change Rate), UACI (Unified Average Changing Intensity), PSNR (Peak Signal-to-Noise Ratio).


2008 ◽  
Vol 281 (17) ◽  
pp. 4254-4260 ◽  
Author(s):  
Fan Ge ◽  
Linfei Chen ◽  
Daomu Zhao

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valli Bhasha A. ◽  
Venkatramana Reddy B.D.

Purpose The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem. Design/methodology/approach This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models. Findings From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images. Originality/value This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.


2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
İlker Altay ◽  
Bilin Aksun Güvenç ◽  
Levent Güvenç

The DriveSafe project was carried out by a consortium of university research centers and automotive OEMs in Turkey to reduce accidents caused by driver behavior. A huge amount of driving data was collected from 108 drivers who drove the instrumented DriveSafe vehicle in the same route of 25 km of urban and highway traffic in Istanbul. One of the sensors used in the DriveSafe vehicle was a forward-looking LIDAR. The data from the LIDAR is used here to determine and record the headway time characteristics of different drivers. This paper concentrates on the analysis of LIDAR data from the DriveSafe vehicle. A simple algorithm that only looks at the forward direction along a straight line is used first. Headway times based on this simple approach are presented for an example driver. A more accurate detection and tracking algorithm taken from the literature are presented later in the paper. Grid-based and point distance-based methods are presented first. Then, a detection and tracking algorithm based on the Kalman filter is presented. The results are demonstrated using experimental data.


2011 ◽  
Vol 145 ◽  
pp. 119-123
Author(s):  
Ko Chin Chang

For general image capture device, it is difficult to obtain an image with every object in focus. To solve the fusion issue of multiple same view point images with different focal settings, a novel image fusion algorithm based on local energy pattern (LGP) is proposed in this paper. Firstly, each focus images is decomposed using discrete wavelet transform (DWT) separately. Secondly, to calculate LGP with the corresponding pixel and its surrounding pixels, then use LGP to compute the new coefficient of the pixel from each transformed images with our proposed weighted fusing rules. The rules use different operations in low-bands coefficients and high-bands coefficients. Finally, the generated image is reconstructed from the new subband coefficients. Moreover, the reconstructed image can represent more detailed for the obtained scene. Experimental results demonstrate that our scheme performs better than the traditional discrete cosine transform (DCT) and discrete wavelet transform (DWT) method in both visual perception and quantitative analysis.


1975 ◽  
Vol 40 (312) ◽  
pp. 405-414 ◽  
Author(s):  
D. K. Bailey ◽  
R. Macdonald

SummaryFluorine, chlorine, zinc, niobium, zirconium, yttrium, and rubidium have been deter-mined on fifteen obsidians from Eburru volcano (Kenya Rift Valley), spanning the range from pantel-leritic trachyte to pantellerite. All pairs of elements show positive correlation coefficients, ranging between 0·769 and 0·998, but with most values better than 0·900. In spite of some very high correlations, only two of the twenty-one best-fit lines pass near the origin of the Cartesian coordinates. Linear distributions are found within two separate groups of elements: F, Zr, Rb; and Cl, Nb, Yt. Zn behaves in general as a member of the second group but seems to be subject to an additional variation. When an element from the fluorine group is plotted against one from the chlorine group the resulting pattern is non-linear. Therefore, although the elements in both groups would generally be considered ‘residual’ (partition coefficients between crystals and liquid approaching zero) there are clearly detectable differences in their variation, and hence their behaviour.Major-element variations in the obsidians are such that a vapour (fluid) phase would be needed to account for any magma evolution. The trace-element patterns are also impossible by closed-system crystal fractionation and suggest that this fluid may have been rich in halogens, with the metallic elements forming preferred ‘complexes’ with either F or Cl. The F-Zr-Rb ‘complex’ also varies quite independently of the important major oxides (e.g. A12O3) in the rocks. In the case of Rb this is but one aspect of a more significant anomaly, in which there is no sign of any influence of alkali feldspar (which partitions Rb) in the variation. This is remarkable because trachytes and rhyolites have normative ab+or > 50 %, and any evolutionary process controlled by crystal ⇋ liquid interactions must be dominated by the melting or crystallization of alkali feldspar. The results on the Eburru obsidians show that if they are an evolutionary series then either, the process was not crystal ⇋ liquid controlled, or that any such process has been overriden (or buffered) by other processes that have superimposed the observed trace-element patterns. In the latter event, the buffering phase may have been a halogen-bearing vapour.The same considerations must apply to other pantellerite provinces where Rb appears to have behaved as a ‘residual’ element.


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