Porosity plays an essential role in petroleum engineering. It controls fluid storage in aquifers, connectivity of the pore structure control fluid flow through reservoir formations. To quantify the relationships between porosity, storage, transport and rock properties, however, the pore structure must be measured and quantitatively described. Porosity estimation of digital image utilizing image processing essential for the reservoir rock analysis since the sample 2D porosity briefly described. The regular procedure utilizes the binarization process, which uses the pixel value threshold to convert the color and grayscale images to binary images. The idea is to accommodate the blue regions entirely with pores and transform it to white in resulting binary image. This paper presents the possibilities of using image processing for determining digital 2D rock samples porosity in carbonate reservoir rocks. MATLAB code created which automatically segment and determine the digital rock porosity, based on the OTSU's thresholding algorithm. In this work, twenty-two samples of 2D thin section petrographic image reservoir rocks of one Iraqi oil field are studied. The examples of thin section images are processed and digitized, utilizing MATLAB programming. In the present study, we have focused on determining of micro and macroporosity of the digital image. Also, some pore void characteristics, such as area and perimeter, were calculated. Digital 2D image analysis results are compared to laboratory core investigation results to determine the strength and restrictions of the digital image interpretation techniques. Thin microscopic image porosity determined using OTSU technique showed a moderate match with core porosity.
CNC machine is used to machine complex or simple shapes at higher speed with maximum accuracy and minimum error. In this paper a previously designed CNC control system is used to machine ellipses and polylines. The sample needs to be machined is drawn by using one of the drawing software like AUTOCAD® or 3D MAX and is saved in a well-known file format (DXF) then that file is fed to the CNC machine controller by the CNC operator then that part will be machined by the CNC machine. The CNC controller using developed algorithms that reads the DXF file feeds to the machine, extracts the shapes from the file and generates commands to move the CNC machine axes so that these shapes can be machined.
The aim of this stud to isolate and identified of A. fumigatus from different sources and study the genetic diversity among these isolates by using RAPD and ISSR markers.Collected 20 samples from 7samples were isolated A. fumigatusisolates were characterized depending on its morphological, then extracted DNA from its.RAPD markersrandomly bandingwith sitesof genome more than ISSR markers where the primer OPN-07 achieved discriminative power (19.1) and 43 bands, while ISSR6 achieved discriminative power (17.1) with 32 bands.ISSR were more efficiency in specific binding then RAPD, ISSR primers has great a binding to production unique band, when 9 primers from 01 primers, ISSR9 was produce (5) unique bands, while RAPD markers was low ability
... Show MoreHuman posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can pre
... Show MoreThe optical absorption data of Hydrogenated Amorphous Silicon was analyzed using a Dunstan model of optical absorption in amorphous semiconductors. This model introduces disorder into the band-band absorption through a linear exponential distribution of local energy gaps, and it accounts for both the Urbach and Tauc regions of the optical absorption edge.Compared to other models of similar bases, such as the O’Leary and Guerra models, it is simpler to understand mathematically and has a physical meaning. The optical absorption data of Jackson et al and Maurer et al were successfully interpreted using Dunstan’s model. Useful physical parameters are extracted especially the band to the band energy gap , which is the energy gap in the a
... Show MoreThe present research aims at recognizing the difficulties and problems which hamper teachers and educators alike when using the internet for educational purposes.It discusses the benefits of the internet as a source of information or publication and as a communicative tool.Arandom sample of (30) teachers working at schools in Baghdad / Second Risafa,was selected.Three of the sample members use the internet for student project plans via internet centers, whereas 16 of them use it for chatting, emailing and research purposes.The rest of the sample have limited knowledge of the internet. The researcher used the interviewing method to gather data from
... Show MoreThis 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
... Show MoreCOVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in