This study examines the vibrations produced by hydropower operations to improve embankment dam safety. This study consists of two parts: In the first part, ANSYS-CFX was used to generate a three-dimensional (3-D) finite volume (FV) model to simulate a vertical Francis turbine unit in the Mosul hydropower plant. The pressure pattern result of the turbine model was transformed into the dam body to show how the turbine unit's operation affects the dam's stability. The upstream reservoir conditions, various flow rates, and fully open inlet gates were considered. In the second part of this study, a 3-D FE Mosul dam model was simulated using an ANSYS program. The operational turbine model's water pressure pattern is conveyed t

A simple setup of random number generator is proposed. The random number generation is based on the shot-noise fluctuations in a p-i-n photodiode. These fluctuations that are defined as shot noise are based on a stationary random process whose statistical properties reflect Poisson statistics associated with photon streams. It has its origin in the quantum nature of light and it is related to vacuum fluctuations. Two photodiodes were used and their shot noise fluctuations were subtracted. The difference was applied to a comparator to obtain the random sequence.

There has been a growing interest in the use of chaotic techniques for enabling secure communication in recent years. This need has been motivated by the emergence of a number of wireless services which require the channel to provide low bit error rates (BER) along with information security. The aim of such activity is to steal or distort the information being conveyed. Optical Wireless Systems (basically Free Space Optic Systems, FSO) are no exception to this trend. Thus, there is an urgent necessity to design techniques that can secure privileged information against unauthorized eavesdroppers while simultaneously protecting information against channel-induced perturbations and errors. Conventional cryptographic techniques are not designed

Chemotherapy is one of the most efficient methods for treating cancer patients. Chemotherapy aims to eliminate cancer cells as thoroughly as possible. Delivering medications to patients’ bodies through various methods, either oral or intravenous is part of the chemotherapy process. Different cell-kill hypotheses take into account the interactions of the expansion of the tumor volume, external drugs, and the rate of their eradication. For the control of drug usage and tumor volume, a model based smooth super-twisting control (MBSSTC) is proposed in this paper. Firstly, three nonlinear cell-kill mathematical models are considered in this work, including the log-kill, Norton-Simon, and hypotheses subject to parametric uncertainties and exo

This paper presents the Extended State Observer (ESO) based repetitive control (RC) for piezoelectric actuator (PEA) based nano-positioning systems. The system stability is proved using Linear Matrix Inequalities (LMIs), which guarantees the asymptotic stability of the system. The ESObased RC used in this paper has the ability to eliminate periodic disturbances, aperiodic disturbances and model uncertainties. Moreover, ESO can be tuned using only two parameters and the model free approach of ESO-based RC, makes it an ideal solution to overcome the challenges of nano-positioning system control. Different types of periodic and aperiodic disturbances are used in simulation to demonstrate the effectiveness of the algorithm. The comparison studi

L1 adaptive controller has proven to provide fast adaptation with guaranteed transients in a large variety of systems. It is commonly used for controlling systems with uncertain time-varying unknown parameters. The effectiveness of  L1 adaptive controller for position control of single axis has been examined and compared with Model Reference Adaptive Controller (MRAC). The Linear servo motor is one of the main constituting elements of the x-y table which is mostly used in automation application. It is characterized by time-varying friction and disturbance.

    The tracking and steady state performances of both controllers have been assessed fo

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

Risks are confronting the foundations of buildings and structures when exposed to earthquakes which leads to high displacements that may cause the failure of the structures. This research elaborates numerically the effect of the earthquake on the vertical and lateral displacement of footing resting on the soil. The thickness of the footing and depth of soil layer below the footing was taken as (0.5, 1.0, and 2.0 m) and (10, 20 and 40m), respectively. The stiffness ratio of soil to footing was also elaborated at 0.68, 0.8, 1.0, and 1.7. The results showed an increase in the verticle displacement of footing as the duration of the earthquake increases. The increase of soil layer thickness below the footing leads to a reduction in the vertical