In this paper, a novel robust blind grayscale image digital watermarking scheme is introduced based on lifting wavelet transform (LWT) in combination with discrete cosine transform (DCT), Hessenberg decomposition, and entropy analysis for copyright protection of multimedia information. At first, the two levels of LWT are applied to the host grayscale image to improve the imperceptibility of the watermarking scheme and then the high-frequency sub-band of the 2-level of LWT is decomposed by DCT. Next, the DCT coefficients are divided into 4× 4 non-overlapping blocks. After that, Hessenberg decomposition performs on each selected block, whereas the first row, first column element of the upper Hessenberg matrix is utilized to hide the watermark. To evaluate the imperceptibility and robustness of the proposed digital watermarking scheme, the peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NC) are utilized to measure the quality and the ability of the proposed watermarking scheme to robust against signal processing operations and geometric attacks. Experimental and analysis results have demonstrated that the proposed scheme is achieved a very good tradeoff between imperceptibility and robustness. The comparison with other scheme have shown that the proposed digital watermarking schemes have a superior performance in terms of imperceptibility and robustness than other

• Abstract: The aim of this study is to investigate some punctuation errors made by English majors at Faculty of Arts, Assab'ah, University of Gheryan. To reach to this goal, the researchers administered a test to a group of 12 students, 2 males and 10 females. The results revealed that the most common errors committed by the participants were: wrong capitalization, misuse of the comma, the inappropriate use of full stop, dropping question marks, and some problems in using hyphen and quotation marks. In the light of the study findings, the researchers suggests that teachers should emphasize the importance of all the punctuation marks in writing. Key words: punctuation, committed, Majors, errors.

The network connectivity is often lost during some situations such as in war zones, battle fields or in regard to natural disasters like floods or earthquakes. Therefore, the connection becomes difficult for rescuing teams to locate people. At the same time, mobile nodes are difficult to be tracked/located. Therefore, flying ad-hoc networks (FANETs) is the solution which provides a way to tackle this situation without the need for any central infrastructure with the use of flying objects (nodes). FANTEs are frequently formed of tiny unmanned aerial vehicles (UAVs) which are flexible, affordable, and rapid to deploy. UAV as a router and without complex hardware deployment, provides an effective real-time communication solution for multiple UAV systems. FANETs can also be used to provide connectivity in such areas in which flying nodes are utilized. In this paper, we discuss the importance of the FANETs and present a routing protocol comparison that depends on the link budget to identify the optimized packet route for the data to be transmitted. The simulation design results show that a throughput enhancement of 0.62Gbps is achieved in comparison to the distance-based routing protocol. The results also confirm the efficiency of the optimized link state routing (OSLR) protocol over the other. The performance of FANETs can be optimized further by using the OLSR protocol.

The aim of the present study was to evaluate the variations in the phytochemical compositions and the antimicrobial activities of Syzygium grande and Oenanthe javanica leaf extracts against Bacillus sp. The antibacterial activity of the methanol, 70% methanol, ethyl acetate, and hexane extracts of both plant species were examined in vitro, against various Bacillus species. In addition, the 1H Nuclear Magnetic Resonance (1H NMR) spectroscopy-based metabolomics was employed to gain insights into the correlations between the chemical constituents of the bioactive extract to the biological activity. The hexane extract of S. grande leaves and methanolic extract of O. javanica leaves showed more potent anti Bacillus activity, among the test extracts. Principal component analysis (PCA) successfully differentiated the extracts on the basis of their metabolite profiles. The results of the Partial Least Square (PLS) analysis showed that the non-polar compounds of the hexane extract of S. grande (alanine, betulin, β-sitosterol, β-caryophyllene, acetic acid and 3-hydroxybutyric acid) were strongly correlated to its anti-Bacillus activity. On the other hand, choline, ellagic acid, and gallic acid, the metabolites present in the methanolic extract of O. javanica, were strongly correlated to its anti-Bacillus activity. On this basis, it was therefore concluded that these conmpounds could be the potential bioactive constituents, contributing to the anti-Bacillus activity of the individual plant species. Further in-depth investigations into the potential utilization of the two plants for useful applications in managing and control of Bacillus sp., will help pave the way towards their valorization.

Counting the number of people in many estimation systems, such as still images or video frames, is a buoyant research area that is challenging in the field of computer vision. It plays a considerable role in a variety of applications, such as security, management, education, and commerce. The purpose of this paper is to suggest a system to estimate the number of people in digital still images based on the Face Detection method. This system composed of two parts: face detection and counting of detected faces. In the detection step, the Skin Color Face Detection method was applied on the input of a digital still image. In the counting part, the obtained detected faces by the Skin Color Face Detection method have counted to estimate the number of people in an input color image with simple software and simple low-cost hardware. The skin color face detection algorithm was tested using 133 images from the People Image Groups dataset, which contains about 2573 color images of people, to test the proposed system. based on the obtained results, the best precision achieved of the proposed Skin Color face detection algorithm was 85%

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.