Mohamed I. Eladawy

A Comparison Between Circular and Hexagonal Array Geometries for Smart Antenna Systems Using Particle Swarm Optimization Algorithm

In this paper, circular and hexagonal array geometries for smart antenna applications are compared. Uniform circular (UCA) and hexagonal arrays (UHA) with 18 half-wave dipole elements are examined; also planar (2 concentric rings of radiators) uniform circular (PUCA) and hexagonal arrays (PUHA) are considered. The effect of rotating the outer ring of the PUCA is studied. In our analysis, the method of moments is used to compute the response of the uniform circular and hexagonal dipole arrays in a mutual coupling environment. The particle swarm optimization (PSO) algorithm is used to optimize the complex excitations, amplitudes and phases, of the adaptive arrays elements for beamforming.

INVESTIGATING THE INTERACTION BETWEEN A HUMAN HEAD AND A SMART HANDSET FOR 4G MOBILE COMMUNICATION SYSTEMS

In this paper we evaluate the potential of a 5-element monopole array incorporated into a handheld device for beamforming in the 5.0-GHz band. The geometry of the handset consists of a 5-element array: four elements located at the handset corners and the fifth-element located at the center. Also, the interaction of the antenna array, mounted on a mobile handset, with a human head phantom is investigated. Firstly, the spatial peak specific absorption rate (SAR) values of 5-element array antennas for mobile handsets in the vicinity of a spherical phantom of a human head are evaluated numerically as a function of the distance between the handset and the head phantom for two different scenarios. Next, the effect of the human head on the handset radiation pattern is studied. The effect of different handset positions on the radiation pattern is also considered. The particle swarm optimization (PSO) algorithm is used to optimize the complex excitations of the adaptive arrays elements in a mutual coupling environment for beamforming synthesis. All numerical simulations are performed using the FEKO Suite 5.3

Performance of Circular Yagi-Uda Arrays for Beamforming Applications Using Particle Swarm Optimization Algorithm

In this paper, a circular Yagi-Uda array (CYUA) for smart antenna applications is designed using the lengths and spacings from the optimum design of a three-element linear Yagi-Uda antenna. Then a modified circular Yagi-Uda array (MCYUA) is developed by adding 5 circular connecting wires to the inner parasitic elements (reflectors) to form a wire grid cylinder. The results are compared with those for uniform circular array (UCA) with 12 half-wave dipole elements. In our analysis, the method of moments (MoM) is used to compute the response of the arrays in a mutual coupling environment. The particle swarm optimization (PSO) algorithm is used to optimize the complex excitations of the adaptive arrays elements for beamforming.

Energy-efficient multi-speed algorithm for scheduling dependent real-time tasks

Reducing energy consumption is a critical issue in the design of battery-powered embedded systems to prolong battery life. With dynamic voltage scaling (DVS) processors, energy consumption can be reduced efficiently by making appropriate decisions on the processor speed/voltage during the scheduling of real time tasks. This paper addresses the problem of energy efficient real-time task scheduling over earliest deadline first (EDF) scheduling policy where the tasks are dependent due to shared resources. Furthermore, the paper proposes enhancements over the existing multi-speed (MS) algorithm where the proposed algorithm achieves more energy saving and has the capability to function with both stack resource policy (SRP) and dynamic priority ceiling protocol (DPCP) as resource access protocols.

Computer Aided Diagnosis of Cardiac Arrhythmias

The early detection of arrhythmia is very important for the cardiac patients. This is done by analyzing the electrocardiogram (ECG) signals and extracting some features from them. These features can be used in the classification of different types of arrhythmias. In this paper, we present three different algorithms of features extraction: Fourier transform (FFT), autoregressive modeling (AR), and principal component analysis (PCA). The used classifier is artificial neural networks (ANN). We observed that the system that depends on the PCA features give the highest accuracy. The proposed techniques deal with the whole 3 second intervals of the training and testing data. We reached the accuracy of 92.7083% compared to 84.4% for the reference that work on a similar data

MPSO-MOM: A Hybrid Modified Particle Swarm Optimization and Method of Moment Algorithm for Smart Antenna Synthesis

Abstract In this article, a modified version of particle swarm optimization (MPSO) and the method of moment (MOM) are combined to achieve the beamforming objective of a practical smart antenna array. The hybrid approach (MPSO–MOM) is illustrated by application to a planar uniform circular array (PUCA) with 30 elements of half-wave dipoles. The array feeding is optimized by MPSO, and the fitness function is evaluated by MOM simulations. The MOM is used to calculate the response of the array in a mutual coupling environment. The performance of the adaptive array using discrete (quantized) feedings is studied. Also in this article, the convergence capability of the MPSO is compared with that of the classical particle swarm optimization and other recent evolutionary-based algorithms using benchmark examples and a linear array synthesis problem.

Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0–F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy.

A multifeature speech/music discrimination system

This paper presents a new technique for the automatic classification of audio signals into either speech or music signal. The classification is based on the most efficient five features extracted from the input signal. The correct classification ratio is always better than that using previous algorithms.

Modified conical silicon nanowires for highly efficient light trapping

A modified nanocone nanowire (NW) is proposed and analyzed for solar cell applications. The suggested NW consists of conical and truncated conical units. The geometrical parameters are studied by using 3D finite difference time domain (FDTD) method to achieve broadband absorption through the reported design and maximize its ultimate efficiency. The analyzed parameters are absorption spectra, ultimate efficiency and short circuit current density. The numerical results prove that the proposed structure is superior compared to cone, truncated cone and cylindrical nanowires (NWs). The reported design achieves an ultimate efficiency of 44.21% with an enhancement of 40.66% relative to the conventional conical NWs. Further, short circuit current density of 36.17 mA/cm2 is achieved by the suggested NW. The modified nanocone has advantages of broadband absorption enhancement, low cost and fabrication feasibility.

Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients

Background/Aim: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning techniques in prediction of advanced fibrosis by combining the serum bio-markers and clinical information to develop the classification models. Methods: A prospective cohort of 39,567 patients with chronic hepatitis C was divided into two sets—one categorized as mild to moderate fibrosis (F0-F2), and the other categorized as advanced fibrosis (F3-F4) according to METAVIR score. Decision tree, genetic algorithm, particle swarm optimization, and multi-linear regression models for advanced fibrosis risk prediction were developed. Receiver operating characteristic curve analysis was performed to evaluate the performance of the proposed models. Results: Age, platelet count, AST, and albumin were found to be statistically significant to advanced fibrosis. The machine learning algorithms under study were able to predict advanced fibrosis in patients with HCC with AUROC ranging between 0.73 and 0.76 and accuracy between 66.3 and 84.4 percent. Conclusions: Machine-learning approaches could be used as alternative methods in prediction of the risk of advanced liver fibrosis due to chronic hepatitis C.