Abstract: Earth velocity changes under many reasons. They cause variations in the length of daytime. These reasons are; interior changes, which usually occur during events that tend to speed up Earth’s rotation slightly but noticeably, and Outward changes, where material that was typically at one point at a higher elevation on Earth surface falls down to be at a lower point. The first reason may show in several figures like earthquake and condensing core of earth. On the other hand, tide, melting glacier poles, and changing weather they cause the other reason. The amount of time that increasing or decreasing in a day until now do not exceed few seconds, but on the massive scale of time as the age of earth it becomes important value. 

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.

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

In this work, we consider Nelder-Mead method to tackle the output

feedbackeigenvalue assignment problem for discrete–time control

systems. First, we reformulate this problem as a constrained minimization

problem. Then by applying apenalty-method approach we find a local

solution to the problem using Nelder-Meadmethod. The performance of

the method is illustrated through some test problemsfrom the literature.

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.

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.

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%

Soil fertilization with wastewater treatment plant (WWTP) biosolids is associated with the introduction of resistance genes (RGs), mobile genetic elements (MGEs) and potentially selective pollutants (antibiotics, heavy metals, disinfectants) into soil. Not much data are available on the parallel analysis of biosolid pollutant contents, RG/MGE abundances and microbial community composition. In the present study, DNA extracted from biosolids taken at 12 WWTPs (two large-scale, six middle-scale and four small-scale plants) was used to determine the abundance of RGs and MGEs via quantitative real-time PCR and the bacterial and archaeal community composition was assessed by 16S rRNA gene amplicon sequencing. Concentrations of heavy metals, antibiotics, the biocides triclosan, triclocarban and quaternary ammonium compounds (QACs) were measured. Strong and significant correlations were revealed between several target genes and concentrations of Cu, Zn, triclosan, several antibiotics and QACs. Interestingly, the size of the sewage treatment plant (inhabitant equivalents) was negatively correlated with antibiotic concentrations, RGs and MGEs abundances and had little influence on the load of metals and QACs or the microbial community composition. Biosolids from WWTPs with anaerobic treatment and hospitals in their catchment area were associated with a higher abundance of potential opportunistic pathogens and higher concentrations of QACs.