<span lang="EN-US">The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in

In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function lnL(φ1, θ1) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (φ1, θ1) for the ARMA(1,1) model. Anot

The total and individual multipole moments of  magnetic electron scattering form factors in ⁴¹Ca  have been investigated using a widely successful model which is the nuclear shell model configurations keeping in mind of 1f_7/2 subshell as an L-S shell and Millinar, Baymann, Zamick as L-S shell (F7MBZ) to give the model space  wave vector. Also, harmonic oscillator wave functions have been used as wave function of a single particle in 1f_7/2  shell. Nucleus ⁴⁰Ca as core closed and Core polarization effects have been used as a corrective with first order correction concept to basic computation of L-S shell and the excitement energy has been implemented with 2ћω. The

Gray-Scale Image Brightness/Contrast Enhancement with Multi-Model
Histogram linear Contrast Stretching (MMHLCS) method

In this research a computational simulation  has been carried out on the design and properties of the electrostatic mirror and a mathematical expression has been suggested  to represent the axial potential of an electrostatic mirror. The electron beam path using the Bimurzaev  technique had been investigated as mirror trajectory with the aid of Runge – Kutta  method. The spherical and chromatic aberration coefficients of mirror has computed and normalized in terms of the focal length. The choice of the mirror depends on the operational requirements. The Electrode shape of mirror two electrodes has been determined by using package SIMION computer program. Computations have shown that the suggested potentials giv

      In this research, the nonparametric technique has been presented to estimate the time-varying coefficients functions for the longitudinal balanced data that characterized by observations obtained through (n) from the independent subjects, each one of them is measured repeatedly by group of  specific time points (m). Although the measurements are independent among the different subjects; they are mostly connected within each subject and the applied techniques is the Local Linear kernel LLPK technique. To avoid the problems of dimensionality, and thick computation, the two-steps method has been used to estimate the coefficients functions by using the two former technique. Since, the two-

The paired sample t-test for testing the difference between two means in paired data is not robust against the violation of the normality assumption. In this paper, some alternative robust tests have been suggested by using the bootstrap method in addition to combining the bootstrap method with the W.M test. Monte Carlo simulation experiments were employed to study the performance of the test statistics of each of these three tests depending on type one error rates and the power rates of the test statistics. The three tests have been applied on different sample sizes generated from three distributions represented by Bivariate normal distribution, Bivariate contaminated normal distribution, and the Bivariate Exponential distribution.