Future University In Egypt (FUE)
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Faculty of Engineering & Technology
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Mostafa Mohamed Salaheldin Abdelkhalek

Basic information

Name : Mostafa Mohamed Salaheldin Abdelkhalek
Title: Assistant lecture
Personal Info: Mostafa Mohamed Salah El Dein Born on October 1, 1991

Education

Certificate Major University Year
Masters . Ain Shams University-Faculty of Engineering 2019
Bachelor Electerical Department Future University - Faculty of Engineering 2013

Researches /Publications

A comprehensive simulation study of hybrid halide perovskite solar cell with copper oxide as HTM - 01/1

Mostafa Mohamed Salaheldin Abdelkhalek

Mohamed Abouelatta, Ahmed Shaker

01/10/2019

Perovskite solar cells (PSCs) have attracted considerable attention as a competitor technology in solar cells due to the rapid enhancement in their power conversion efficiency (PCE) in recent years. PSCs have several advantages such as their bandgap tunability, lower cost, tolerance of high impurities, protracted diffusion length and wide optical absorption. In this paper, simulation of PSCs with copper oxide as a hole transport material (HTM) and different electron transport materials (ETMs) has been presented. The proposed materials are a replacement to the ordinary hole and ETMs; such as the titanium dioxide and the expensive spiro-OMeTAD. In addition, a comprehensive study for optimizing the features and parameters of the PSCs, such as the thickness and defect density of the perovskite layer, the doping concentrations, and the bandgap energy, has been introduced. The simulation and the performance evaluation of the designed PSCs have been carried out using SCAPS-1D. The results show that mixed halide PSC with zinc oxysulfide as ETM and copper oxide as HTM has an enhanced performance with a PCE of up to 30.82%.

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Parametric Fault Detection of Analogue Circuits Based on Optimized Support Vector Machine Classifier - 01/1

Mostafa Mohamed Salaheldin Abdelkhalek

Hossam E. Abou-Bakr Hassan

01/12/2018

Parametric faults in analogue circuits cause system performance degeneration and are hard to be detected. There are no clear boundaries between fault-free and faulty circuit output due to components tolerances. Therefore, a machine learning classifier needs to be learned to correctly classify circuit outputs. In this paper, the parametric fault detection method of analogue circuits based on the support vector machine (SVM) classifier is developed. The proper choice of kernel parameters for the SVM in the training process improves the classification accuracy. The penalty parameter and kernel function parameters for the radial basis function (RBF) kernel should be optimized. In addition, the Bayesian optimization methodology is used to select the hyperparameters for the SVM classifier. The Biquad filter, one of the benchmark circuits, is utilized to validate the proposed method and compare it with the other methods. Using downside minimum size detectable fault (DMSDF) and upside minimum size detectable fault (UMSDF) values, the method gives good enhancements in detecting faults due to minor changes in components values above or down the nominal component values.

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