This project presents an enhanced Iraqi vehicle plate identification system, incorporating digital processing technologies and the XOR Auto-Associative Memory advanced associative memory algorithm, towards a fast and efficient performance under diverse conditions of imagery. The system consists of three major stages: identification of the panel area, or localization, using edge transformations and morphological operations; symbol segmentation, or segmentation, based on horizontal and vertical projections for the exact extraction of numbers and letters; and finally, recognition use of XOR operations and a binary distance criterion known as Hamming Distance. The model was tested on 420 images of panels in different scenarios of lighting: day, night, shadows, fog, and low light. It produced an accuracy of 98.10% in identifying the panel area and 98.14% in segmenting symbols, while the XOR algorithm gave the best result in recognizing 2,958 symbols with an accuracy of 99.93% in 2.4 ms per symbol. Another contribution of this system is that it has proven to be able to work in real time, processing 15–18 panels per second. These results confirm the efficiency of the proposed algorithm in dealing with distorted or low-quality images and stand out as a simplified and effective alternative to the traditional methods and old associative memory algorithms. Therefore, this research provides a practical and reliable framework for identifying Iraqi vehicle plates in security and traffic applications with high time requirements.
In this work an experimental simulation is made to predict the performance of steady-state natural heat convection along heated finned vertical base plate to ambient air with different inclination angles and configurations of fin array. Two types of fin arrays namely vertical fins array and V-fins array on heated vertical base plate are used with different heights and spaces. The influence of inclination angle of the plate , configuration of fins array and fin geometrical parameters such as fin height and fin spacing on the temperature distribution, base convection heat transfer coefficient and average Nusselt number have been plotted and discussed. The experimental data are correlated to a formula between average Nusselt number versus R
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Regression testing is a crucial phase in the software development lifecycle that makes sure that new changes/updates in the software system don’t introduce defects or don’t affect adversely the existing functionalities. However, as the software systems grow in complexity, the number of test cases in regression suite can become large which results into more testing time and resource consumption. In addition, the presence of redundant and faulty test cases may affect the efficiency of the regression testing process. Therefore, this paper presents a new Hybrid Framework to Exclude Similar & Faulty Test Cases in Regression Testing (ETCPM) that utilizes automated code analysis techniques and historical test execution data to
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Evaluation of the Antibacterial Efficacy of Electrolyzed Oxidizing Water as an Irrigant against Enterococcus faecalis (An In vitro Study), Noor A Khait*, Muna Saleem Kalaf
This paper presents on the design of L-Band Multiwavelength laser for Hybrid Time Division Multiplexing/ Wavelength Division Multiplexing (TDM/WDM) Passive Optical Network (PON) application. In this design, an L-band Mulltiwavelength Laser is designed as the downstream signals for TDM/WDM PON. The downstream signals ranging from 1569.865 nm to 1581.973 nm with 100GHz spacing. The multiwavelength laser is designed using OptiSystem software and it is integrated into a TDM/WDM PON that is also designed using OptiSystem simulation software. By adapting multiwavelength fiber laser into a TDM/WDM network, a simple and low-cost downstream signal is proposed. From the simulation design, it is found that the proposed design is suitable to be used
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<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c
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The aim of this research is to know how business organizations achieve competitive advantage ,and make it sustainable through constructing a green strategy ( friend to environment) which is reflected on sustaining their competitive advantages .The problem of this study is presented through trying to answer many thoughtful questions, the most important of them are:
1-Can business organizations today make green strategies supporting their competitive advantage?
2-Is there a framework or mechanism could be depended on by business organizations to manage strategic risks of losing their competit
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The study aims to find out the extent to which several Iraqi institutional accreditation standards (governance and administration, scientific research, curricula) are applied in two public universities (Baghdad and Middle Technical University) and two private universities (Uruk and Al-Mansour College) by diagnosing strengths and weaknesses and proposing a mechanism and procedures to help educational institution aims to reduce or eliminate the gap. The study stems from the extent of application of several Iraqi institutional accreditation standards represented as it was worked on through observation and field coexistence to reach scientific and practical facts. The method of case study and comparison betwe
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