This paper describes a microcontroller-based function generator system. By the function generator sine wave, square wave, quasi-square wave, saw-tooth and triangular waveforms are generated over a wide frequency range according to user requirements. By utilizing processing capabilities of the microcontroller the hardware is minimized exceedingly. The output waveform shapes are digitally-controlled to achieve the required wave shape. The single chip microcomputer of waveform generation equipment offers the possibility of improvements in manufacture reliability, maintenance and servicing and increased control flexibility. The system is built and tested. The results of test were satisfactory and appreciated by test engineers at different ce

This paper includes the application of Queuing theory with of Particle swarm algorithm or is called (Intelligence swarm) to solve the problem of The queues and developed for General commission for taxes /branch Karkh center in the service stage of the Department of calculators composed of six  employees , and it was chosen queuing model is a single-service channel  M / M / 1 according to the nature of the circuit work mentioned above and it will be divided according to the letters system for each employee, and  it was composed of data collection times (arrival time , service time, departure time)

In this paper, we derived an estimator of reliability function for Laplace distribution with two parameters using Bayes method with square error loss function, Jeffery’s formula and conditional probability random variable of observation. The main objective of this study is to find the efficiency of the derived Bayesian estimator compared to the maximum likelihood of this function and moment method using simulation technique by Monte Carlo method under different Laplace distribution parameters and sample sizes. The consequences have shown that Bayes estimator has been more efficient than the maximum likelihood estimator and moment estimator in all samples sizes

This paper deals with defining Burr-XII, and how to obtain its p.d.f., and CDF, since this distribution is one of failure distribution which is compound distribution from two failure models which are Gamma model and weibull model. Some equipment may have many important parts and the probability distributions representing which may be of different types, so found that Burr by its different compound formulas is the best model to be studied, and estimated its parameter to compute the mean time to failure rate. Here Burr-XII rather than other models is consider  because it is used to model a wide variety of phenomena including crop prices, household income, option market price distributions, risk and travel time. It has two shape-parame

A non-parametric kernel method with Bootstrap technology was used to estimate the confidence intervals of the system failure function of the log-normal distribution trace data. These are the times of failure of the machines of the spinning department of the weaving company in Wasit Governorate. Estimating the failure function in a parametric way represented by the method of the maximum likelihood estimator (MLE). The comparison between the parametric and non-parametric methods was done by using the average of Squares Error (MES) criterion. It has been noted the efficiency of the nonparametric methods based on Bootstrap compared to the parametric method. It was also noted that the curve estimation is more realistic and appropriate for the re

This paper focuses on Load distribution factors for horizontally curved composite concrete-steel girder bridges. The finite-element analysis software“SAP2000” is used to examine the key parameters that can influence the distribution factors for horizontally curved composite steel
girders. A parametric study is conducted to study the load distribution characteristics of such bridge system due to dead loading and AASHTO truck loading using finite elements method. The key parameters considered in this study are: span-to-radius of curvature ratio, span length, number of girders, girders spacing, number of lanes, and truck loading conditions. The results have shown that the curvature is the most critical factor which plays an important

Spatial data observed on a group of areal units is common in scientific applications. The usual hierarchical approach for modeling this kind of dataset is to introduce a spatial random effect with an autoregressive prior. However, the usual Markov chain Monte Carlo scheme for this hierarchical framework requires the spatial effects to be sampled from their full conditional posteriors one-by-one resulting in poor mixing. More importantly, it makes the model computationally inefficient for datasets with large number of units. In this article, we propose a Bayesian approach that uses the spectral structure of the adjacency to construct a low-rank expansion for modeling spatial dependence. We propose a pair of computationally efficient estimati

Background: Salivary tumors are uncommon, being of low incidence worldwide. This study aimed to assess cases collected in this series of salivary gland tumors in regard to histopathological typing, in relation to age, site and gender. Materials and methods: This is a retrospective study; cases were collected from public and private laboratories. A total number of 171 cases were collected. The slides were reviewed and reclassified for histopathological typing according to WHO classification 2005. Results: Benign tumors were more common than malignant tumors. The most common histological type was benign mixed tumor, followed by Warthin’s tumor. The most common malignant tumor was adenoid cystic carcinoma. One hundred twenty three cases ou