Image is an important digital information that used in many internet of things (IoT) applications such as transport, healthcare, agriculture, military, vehicles and wildlife. etc. Also, any image has very important characteristic such as large size, strong correlation and huge redundancy, therefore, encrypting it by using single key Advanced Encryption Standard (AES) through IoT communication technologies makes it vulnerable to many threats, thus, the pixels that have the same values will be encrypted to another pixels that have same values when they use the same key. The contribution of this work is to increase the security of transferred image. This paper proposed multiple key AES algorithm (MECCAES) to improve the security of the transmitted image through IoT. This approach is evaluated via applying it on RGB bmp images and analyzing the results using standard metrics such as entropy, histogram, correlation, Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MES) metrics. Also, the time for encryption and decryption for the proposed MECCAES is the same time consumed by original single key AES is 12 second(the used image size is 12.1MB therefore time is long). The performance experiments show that this scheme achieves confidentiality also it encourages to use effectively in a wide IoTs fields to secure transmitted image.
A simulation study of using 2D tomography to reconstruction a 3D object is presented. The 2D Radon transform is used to create a 2D projection for each slice of the 3D object at different heights. The 2D back-projection and the Fourier slice theorem methods are used to reconstruction each 2D projection slice of the 3D object. The results showed the ability of the Fourier slice theorem method to reconstruct the general shape of the body with its internal structure, unlike the 2D Radon method, which was able to reconstruct the general shape of the body only because of the blurring artefact, Beside that the Fourier slice theorem could not remove all blurring artefact, therefore, this research, suggested the threshold technique to eliminate the
... Show MoreColor image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and
<p>In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on
... Show MoreArtificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le
... Show MoreThe confirming of security and confidentiality of multimedia data is a serious challenge through the growing dependence on digital communication. This paper offers a new image cryptography based on the Chebyshev chaos polynomials map, via employing the randomness characteristic of chaos concept to improve security. The suggested method includes block shuffling, dynamic offset chaos key production, inter-layer XOR, and block 90 degree rotations to disorder the correlations intrinsic in image. The method is aimed for efficiency and scalability, accomplishing complexity order for n-pixels over specific cipher rounds. The experiment outcomes depict great resistant to cryptanalysis attacks, containing statistical, differential and brut
... Show MoreIslamic Culture face many challenges Such as، Secularism، westrenism، globalism and Colonialism under current attempts For western States and united states of America to dominate on world and Confront all her Opponent Polaris it be clear when U. S. A. Occupied Afghanistan and Iraq and Threatened many Arabic and Islamic States
Islamic Culture face many challenges Such as، Secularism، westrenism، globalism and Colonialism under current attempts For western States and united states of America to dominate on world and Confront all her Opponent Polaris it be clear when U. S. A. Occupied Afghanistan and Iraq and Threatened many Arabic and Islamic States
An efficient modification and a novel technique combining the homotopy concept with Adomian decomposition method (ADM) to obtain an accurate analytical solution for Riccati matrix delay differential equation (RMDDE) is introduced in this paper . Both methods are very efficient and effective. The whole integral part of ADM is used instead of the integral part of homotopy technique. The major feature in current technique gives us a large convergence region of iterative approximate solutions .The results acquired by this technique give better approximations for a larger region as well as previously. Finally, the results conducted via suggesting an efficient and easy technique, and may be addressed to other non-linear problems.