Image pattern classification is considered a significant step for image and video processing.Although various image pattern algorithms have been proposed so far that achieved adequate classification,achieving higher accuracy while reducing the computation time remains challenging to date. A robust imagepattern classification method is essential to obtain the desired accuracy. This method can be accuratelyclassify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism.Moreover, to date, most of the existing studies are focused on evaluating their methods based on specificorthogonal moments, which limits the understanding of their potential application to various DiscreteOrthogonal Moments (DOMs). The

Starting from 4, - Dimercaptobiphenyl, a variety of phenolic Schiff bases (methylolic, etheric, epoxy) derivatives have been synthesized. All proposed structure were supported by FTIR, 1H-NMR, 13C-NMR Elemental analysis all analysis were performed in center of consultation in Jordan Universty.

Computer systems and networks are being used in almost every aspect of our daily life; as a result the security threats to computers and networks have also increased significantly. Traditionally, password-based user authentication is widely used to authenticate legitimate user in the current system0T but0T this method has many loop holes such as password sharing, shoulder surfing, brute force attack, dictionary attack, guessing, phishing and many more. The aim of this paper is to enhance the password authentication method by presenting a keystroke dynamics with back propagation neural network as a transparent layer of user authentication. Keystroke Dynamics is one of the famous and inexpensive behavioral biometric technologies, which identi

In this paper, a method is proposed to increase the compression ratio for the color images by
dividing the image into non-overlapping blocks and applying different compression ratio for these
blocks depending on the importance information of the block. In the region that contain important
information the compression ratio is reduced to prevent loss of the information, while in the
smoothness region which has not important information, high compression ratio is used .The
proposed method shows better results when compared with classical methods(wavelet and DCT).

Channel estimation and synchronization are considered the most challenging issues in Orthogonal Frequency Division Multiplexing (OFDM) system. OFDM is highly affected by synchronization errors that cause reduction in subcarriers orthogonality, leading to significant performance degradation. The synchronization errors cause two issues: Symbol Time Offset (STO), which produces inter symbol interference (ISI) and Carrier Frequency Offset (CFO), which results in inter carrier interference (ICI). The aim of the research is to simulate Comb type pilot based channel estimation for OFDM system showing the effect of pilot numbers on the channel estimation performance and propose a modified estimation method for STO with less numb

Accurate emotion categorization is an important and challenging task in computer vision and image processing fields. Facial emotion recognition system implies three important stages: Prep-processing and face area allocation, feature extraction and classification. In this study a new system based on geometric features (distances and angles) set derived from the basic facial components such as eyes, eyebrows and mouth using analytical geometry calculations. For classification stage feed forward neural network classifier is used. For evaluation purpose the Standard database "JAFFE" have been used as test material; it holds face samples for seven basic emotions. The results of conducted tests indicate that the use of suggested distances, angles

In this work we present a technique to extract the heart contours from noisy echocardiograph images. Our technique is based on improving the image before applying contours detection to reduce heavy noise and get better image quality. To perform that, we combine many pre-processing techniques (filtering, morphological operations, and contrast adjustment) to avoid unclear edges and enhance low contrast of echocardiograph images, after implementing these techniques we can get legible detection for heart boundaries and valves movement by traditional edge detection methods.

Producing pseudo-random numbers (PRN) with high performance is one of the important issues that attract many researchers today. This paper suggests pseudo-random number generator models that integrate Hopfield Neural Network (HNN) with fuzzy logic system to improve the randomness of the Hopfield Pseudo-random generator. The fuzzy logic system has been introduced to control the update of HNN parameters. The proposed model is compared with three state-ofthe-art baselines the results analysis using National Institute of Standards and Technology (NIST) statistical test and ENT test shows that the projected model is statistically significant in comparison to the baselines and this demonstrates the competency of neuro-fuzzy based model to produce