In this paper, we characterize the percolation condition for a continuum secondary cognitive radio network under the SINR model. We show that the well-established condition for continuum percolation does not hold true in the SINR regime. Thus, we find the condition under which a cognitive radio network percolates. We argue that due to the SINR requirements of the secondaries along with the interference tolerance of the primaries, not all the deployed secondary nodes necessarily contribute towards the percolation process- even though they might participate in the communication process. We model the invisibility of such nodes using the concept of Poisson thinning, both in the presence and absence of primaries. Invisibility occurs due to nodes that i) cannot decode transmissions except from their nearest neighbors, ii) are always interfered, and iii) belong to isolated components. We find the thinning probability in terms of primary and secondary densities, communication radii, and interference cancellation coefficient. Further, we show how the effective coverage radius shrinks which also adds to the thinning. Theoretical findings are validated through simulations.
The Zubair reservoir in the Abu-Amood field is considered a shaly sand reservoir in the south of Iraq. The geological model is created for identifying the facies, distributing the petrophysical properties and estimating the volume of hydrocarbon in place. When the data processing by Interactive Petrophysics (IP) software is completed and estimated the permeability reservoir by using the hydraulic unit method then, three main steps are applied to build the geological model, begins with creating a structural, facies and property models. five zones the reservoirs were divided (three reservoir units and two cap rocks) depending on the variation of petrophysical properties (porosity and permeability) that results from IP software interpr
... Show MoreClassifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area. The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and
... Show MoreGeomechanical modelling and simulation are introduced to accurately determine the combined effects of hydrocarbon production and changes in rock properties due to geomechanical effects. The reservoir geomechanical model is concerned with stress-related issues and rock failure in compression, shear, and tension induced by reservoir pore pressure changes due to reservoir depletion. In this paper, a rock mechanical model is constructed in geomechanical mode, and reservoir geomechanics simulations are run for a carbonate gas reservoir. The study begins with assessment of the data, construction of 1D rock mechanical models along the well trajectory, the generation of a 3D mechanical earth model, and runni
New complexes have been prepared from the new ligand [2,2′‐(5,5‐dimethylcyclohexane‐1,3‐diylidene)bis(azan‐1‐yl‐1‐ylidene)dibenzoic acid] derived from 5,5‐dimethylcyclohexane‐1,3‐dione and 2‐aminobenzoic acid. Accordingly, its mono and binuclear Mn(II), Co(II), Cu(II), Zn(II), and Cd(II) complexes were prepared. The prepared components have been characterized by various spectroscopic techniques and elemental analysis. The thermal stability of the ligand and its complexes were performed by TGA. It was found that all the complexes have excellent thermal stability and do not contain water molecules within their structure, but the ligand has little stability. Additionally, theor
With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se
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