The calibration of a low-speed wind tunnel (LSWT) test section had been made in the present work. The tunnel was designed and constructed at the Aerodynamics Lab. in the Mechanical Engineering Department/University of Baghdad. The test section design speed is 70 m/s. Frictional loses and uniformity of the flow inside the test section had been tested and calibrated based on the British standards for flow inside ducts and conduits. Pitot-static tube, boundary layer Pitot tube were the main instruments which were used in the present work to measure the flow characteristics with emphasize on the velocity uniformity and boundary layer growth along the walls of the test section. It is found that the maximum calibrated velocity for empty test sect

The calibration of a low-speed wind tunnel (LSWT) test section had been made in the present work. The tunnel was designed and constructed at the Aerodynamics Lab. in the Mechanical Engineering Department/University of Baghdad. The test section design speed is 70 m/s. Frictional loses and uniformity of the flow inside the test section had been tested and calibrated based on the British standards for flow inside ducts and conduits. Pitot-static tube, boundary layer Pitot tube were the main instruments which were used in the present work to measure the flow characteristics with emphasize on the velocity uniformity and boundary layer growth along the walls of the test section. It is found that the maximum calibrated velocity for empty test s

In this paper, we calculate and measure the SNR theoretically and experimental for digital full duplex optical communication systems for different ranges in free space, the system consists of transmitter and receiver in each side. The semiconductor laser (pointer) was used as a carrier wave in free space with the specification is 5mW power and 650nm wavelength. The type of optical detector was used a PIN with area 1mm2 and responsively 0.4A/W for this wavelength. The results show a high quality optical communication system for different range from (300-1300)m with different bit rat (60-140)kbit/sec is achieved with best values of the signal to noise ratio (SNR).

This research  describes the design & implementation of frequency synthesizer using single loop Phase lock loop with the following specifications: Frequency range (1.5 – 2.75) GHz,Step size (1 MHz), Switching time 36.4 µs, & phase noise @10 kHz = -92dBc & spurious -100 dBc

  The development in I.C. technology provide the simplicity in the design of frequency synthesizer because it implements the phase frequency detector(PFD) , prescalar &  reference divider in single chip. Therefore our system consists of  a single chip contains (low phase noise PFD, charge pump, prescalar  & reference divider), voltage controlled oscillator , loop filter & reference oscillator. The single chip

in this paper fourth order kutta method has been used to find the numerical solution for different types of first liner

In this paper, the author established some new integral conditions for the oscillation of all solutions of nonlinear first order neutral delay differential equations. Examples are inserted to illustrate the results.

This work describes an experimental setup to evaluate the photodynamictoxicity of 650 nm diode laser and 532 nm Frequency-doubled Q-Switched Nd:YAG laser on the growth of Candida albicans as well as the potential fungicidal effect when combining the laser irradiation with specific photosensitizers namely methylene blue, toluidine blue, acridine orange and safranin O. In this study the findings showed that the number of colony-forming units per millilitre (CFU/ml) of C. albicans decreased with increasing exposure time. In particular in the case of the frequency doubled Nd:YAG laser combined with safranin O, the best lethal effect occurred at 11 minutes exposure time with 2.26 J/cm² energy density (89.18% reduction) in comparison with the

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance