The Maxwell equations have been formulated for a composite slab waveguide at x-band wave propagation. The eigenvalues of the system equations are obtained by using MATLAB program. These eigenvalues are used to obtain the wave propagation constant and a number of modes inside the slabs. A good correspondence was seen between the number of modes and the cut off thickness. The parameter that affects the performance of waveguide is the slab thickness. The propagation constant is usually adopted to characterize this type of waveguide and show how the cutoff frequency of the mode in the slab is increased dramatically by decreasing the frequency.
Our study focused on lower modes, the results for the transmission coefficient are then used to study the propagation properties of the guided modes in waveguides. Characteristic equations, solutions for TE (transverse electric) modes are presented. Numerical results for the propagation constant and field distributions of several lower-order modes are presented.
By finding the exact transmission coefficient of lower mode of a plane wave incident at a normal incidence on composite slabs, the propagation properties of guided modes in planar slab were studied.
It is important to find suitable materials for the transmitting and receiving of microwave frequencies and useful for designing a waveguide filter to separate waves of the same frequency but with different transmission modes.
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One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p
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The paper generates a geological model of a giant Middle East oil reservoir, the model constructed based on the field data of 161 wells. The main aim of the paper was to recognize the value of the reservoir to investigate the feasibility of working on the reservoir modeling prior to the final decision of the investment for further development of this oilfield. Well log, deviation survey, 2D/3D interpreted seismic structural maps, facies, and core test were utilized to construct the developed geological model based on comprehensive interpretation and correlation processes using the PETREL platform. The geological model mainly aims to estimate stock-tank oil initially in place of the reservoir. In addition, three scenarios were applie
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<span>As a result of numerous applications and low installation costs, wireless sensor networks (WSNs) have expanded excessively. The main concern in the WSN environment is to lower energy consumption amidst nodes while preserving an acceptable level of service quality. Using multi-mobile sinks to reduce the nodes' energy consumption have been considered as an efficient strategy. In such networks, the dynamic network topology created by the sinks mobility makes it a challenging task to deliver the data to the sinks. Thus, in order to provide efficient data dissemination, the sensor nodes will have to readjust the routes to the current position of the mobile sinks. The route re-adjustment process could result in a significant m
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