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Improving the BURT’s Sensitivity using Noise Calibration Unit via Crab Nebula Observations
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Radio observations from astronomical sources like supernovae became one the most important sources of information about the physical properties of those objects. However, such radio observations are affected by various types of noise such as those from sky, background, receiver, and the system itself. Therefore, it is essential to eliminate or reduce these undesired noise from the signals in order to ensure accurate measurements and analysis of radio observations. One of the most commonly used methods for reducing the noise is to use a noise calibrator. In this study, the 3-m Baghdad University Radio Telescope (BURT) has been used to observe crab nebula with and without using a calibration unit in order to investigate its impact on the signal. Radio observations of crab nebula have been carried out for different periods in 2022. Several parameters of the telescope have been calculated and analyzed using statistical measurements with and without using the noise calibration unit. Those parameters are receiver gain, system temperature, antenna temperature, and degree per flux unit. The results of this research revealed that the fluctuation sensitivity of BURT improved by about an order of magnitude, when the noise calibration unit is used. The root mean square error and the radiometer equation of the antenna temperature decreased to less than 33% and 10%, respectively in comparison to their initial values. In conclusion, the noise calibration unit plays a crucial role to improve the sensitivity of a radio telescope drastically.

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Publication Date
Thu Dec 01 2022
Journal Name
Iraqi Journal Of Science
PLAGIARISM DETECTION SYSTEM IN SCIENTIFIC PUBLICATION USING LSTM NETWORKS
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Publication Date
Wed Oct 25 2023
Journal Name
Plos One
Performance enhancement of high order Hahn polynomials using multithreading
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Orthogonal polynomials and their moments have significant role in image processing and computer vision field. One of the polynomials is discrete Hahn polynomials (DHaPs), which are used for compression, and feature extraction. However, when the moment order becomes high, they suffer from numerical instability. This paper proposes a fast approach for computing the high orders DHaPs. This work takes advantage of the multithread for the calculation of Hahn polynomials coefficients. To take advantage of the available processing capabilities, independent calculations are divided among threads. The research provides a distribution method to achieve a more balanced processing burden among the threads. The proposed methods are tested for va

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Publication Date
Wed Apr 28 2021
Journal Name
2021 1st Babylon International Conference On Information Technology And Science (bicits)
Enhanced Twitter Community Detection using Node Content and Attributes
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Publication Date
Sat Jan 01 2022
Journal Name
1st Samarra International Conference For Pure And Applied Sciences (sicps2021): Sicps2021
Feeble Hausdorff spaces in alpha-topological spaces using graph
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Publication Date
Sun Mar 29 2020
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Dissolving Precipitated Asphaltenes Inside Oil Reservoirs Using Local Solvents
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There are several oil reservoirs that had severe from a sudden or gradual decline in their production due to asphaltene precipitation inside these reservoirs. Asphaltene deposition inside oil reservoirs causes damage for permeability and skin factor, wettability alteration of a reservoir, greater drawdown pressure. These adverse changing lead to flow rate reduction, so the economic profit will drop. The aim of this study is using local solvents: reformate, heavy-naphtha and binary of them for dissolving precipitated asphaltene inside the oil reservoir. Three samples of the sand pack had been prepared and mixed with a certain amount of asphaltene. Permeability of these samples calculated before and after mixed with asphaltenes. Then, the

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Publication Date
Sun Feb 25 2024
Journal Name
Tikrit Journal Of Pure Science
Optical Mark Recognition using Modify Bi-directional Associative Memory
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Optical Mark Recognition (OMR) is an important technology for applications that require speedy, high-accuracy processing of a huge volume of hand-filled forms. The aim of this technology is to reduce manual work, human effort, high accuracy in assessment, and minimize time for evaluation answer sheets. This paper proposed OMR by using Modify Bidirectional Associative Memory (MBAM), MBAM has two phases (learning and analysis phases), it will learn on the answer sheets that contain the correct answers by giving its own code that represents the number of correct answers, then detection marks from answer sheets by using analysis phase. This proposal will be able to detect no selection or select more than one choice, in addition, using M

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Publication Date
Thu Jun 01 2023
Journal Name
Sustainable Engineering And Innovation
A review of enhanced image techniques using chaos encryption
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Secured multimedia data has grown in importance over the last few decades to safeguard multimedia content from unwanted users. Generally speaking, a number of methods have been employed to hide important visual data from eavesdroppers, one of which is chaotic encryption. This review article will examine chaotic encryption methods currently in use, highlighting their benefits and drawbacks in terms of their applicability for picture security.

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Publication Date
Wed Sep 15 2021
Journal Name
2021 International Conference On Computing And Communications Applications And Technologies (i3cat)
Parallel Hybrid String Matching Algorithm Using CUDA API Function
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Publication Date
Wed Oct 06 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Image segmentation by using thresholding technique in two stages
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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Journal Of Computer, Communication, Control And System Engineering
A Framework for Predicting Airfare Prices Using Machine Learning
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Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

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