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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
Fri Mar 01 2024
Journal Name
Water, Air, & Soil Pollution
Decontamination of Cobalt-Polluted Soils Using an Enhanced Electro-kinetic Method, Employing Eco-friendly Conditions
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Publication Date
Tue Jul 30 2024
Journal Name
Iraqi Journal Of Science
Frame-Based Change Detection Using Histogram and Threshold to Separate Moving Objects from Dynamic Background
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      Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.

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Publication Date
Tue Jun 01 2021
Journal Name
Iop Conference Series: Earth And Environmental Science
Performance Evaluation of Al-Karkh Water Treatment Plant Using Model-driven and Data-Driven Models
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Abstract<p>There is a great operational risk to control the day-to-day management in water treatment plants, so water companies are looking for solutions to predict how the treatment processes may be improved due to the increased pressure to remain competitive. This study focused on the mathematical modeling of water treatment processes with the primary motivation to provide tools that can be used to predict the performance of the treatment to enable better control of uncertainty and risk. This research included choosing the most important variables affecting quality standards using the correlation test. According to this test, it was found that the important parameters of raw water: Total Hardn</p> ... Show More
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Publication Date
Sat Apr 30 2022
Journal Name
Eastern-european Journal Of Enterprise Technologies
Improvement of noisy images filtered by bilateral process using a multi-scale context aggregation network
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Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

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Publication Date
Sun Dec 09 2018
Journal Name
Baghdad Science Journal
Anticorrosion Behavior of Deposited Magnetite on Galvanized Steel in Saline Water Using RF-Magnetron Sputtering
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Thin films of Magnetite have been deposited on Galvanized Steel (G-S) alloy using RF-reactive magnetron sputtering technique and protection efficiency of the corrosion of G-S. A Three-Electrodes Cell was used in saline water (3.5 % NaCl) solution at different temperatures (298, 308, 318 & 328K) using potentiostatic techniques with. Electrochemical Impedance Spectroscopy (EIS) and fitting impedance data via Frequency Response Analysis (FRA) were applied to G-S alloy with Fe3O4 and tested in 3.5 % NaCl solution at 298K.Results taken from Nyquist and Bode plots were analyzed using software provided with the instrument. The results obtained show that the rate of corrosion of G.S alloy increased with increasing the temperatures from 298 t

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Publication Date
Tue Jul 17 2018
Journal Name
International Journal Of Adaptive Control And Signal Processing
Single channel informed signal separation using artificial-stereophonic mixtures and exemplar-guided matrix factor deconvolution
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Publication Date
Mon Mar 23 2020
Journal Name
Journal Of Engineering
Diagnosing Water Problem for Asmari Reservoir in Abu Ghirab Oilfield Using Analytical and Numerical Approaches
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Asmari is the main productive reservoir in Abu Ghirab oilfield in the south-east part of Iraq. It has history production extends from 1976 up to now with several close periods. Recently, the reservoir suffers some problems in production, which are abstracted as water production rising with oil production declining in most wells. The water problem type of the field and wells is identified by using Chan's diagnostic plots (water oil ratio (WOR) and derivative water oil ratio (WOR') against time). The analytical results show that water problem is caused by the channeling due to high permeability zones, high water saturation zones, and faults or fracturing. The numerical approach is also used to study the water movement inside the reser

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Publication Date
Mon Sep 01 2014
Journal Name
Paripex-indian Journal Of Research
Inundation Map Development by Using HEC-RAS Hydraulic Simulation Modeling from Roseires to Khartoum Cities
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Inundation floodingmap aimedto find outearly warningsto avoidenvironmental damageandhumanin terms of theheight ofthe wave ofwater, speed time arrival, effects of inundation sideanddepth of the water/ distanceand reduce the impact of the flood wave after obtaining the process of collapse of the dam in the lower part of the river to the dam area. The study has been using a numerical model one-dimensional depends on the development of equations (Saint-Venant) so that parts of the river, any river channel main banks of the right and left treated as separate parts, that’s the difference in the characteristics of the hydraulic and engineering, along the line of the flow will take into account in each section of the sections and flow in the riv

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Publication Date
Wed Feb 22 2023
Journal Name
Iraqi Journal Of Science
Extraction Drainage Network for Lesser Zab River Basin from DEM using Model Builder in GIS
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ArcHydro is a model developed for building hydrologic information systems to synthesize geospatial and temporal water resources data that support hydrologic modeling and analysis. Raster-based digital elevation models (DEMs) play an important role in distributed hydrologic modeling supported by geographic information systems (GIS). Digital Elevation Model (DEM) data have been used to derive hydrological features, which serve as inputs to various models. Currently, elevation data are available from several major sources and at different spatial resolutions. Detailed delineation of drainage networks is the first step for many natural resource management studies. Compared with interpretation from aerial photographs or topographic maps, auto

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Publication Date
Mon Jun 30 2014
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Using Sonic Log to Predict Abnormal Pressure Zones in Selected Oil Wells (Western of Iraq)
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Two oil wells were tested to find the abnormal pressure zones using sonic log technique. We found that well Abu-Jir-3 and Abu-Jir-5 had an abnormal pressure zones from depth 4340 to 4520 feet and 4200 to 4600 feet, respectively. The maximum difference between obtained results and the field measured results did not exceed 2.4%.
In this paper, the formation pressures were expressed in terms of pressure gradient which sometimes reached up to twice the normal pressure gradient.
Drilling and developing such formations were dangerous and expensive.
The plotted figures showed a clear derivation from the normal trend which confirmed the existence of abnormal pressure zones.

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