Experimental determination of elastic modulus of elasticity and Poisson’s coefficient of date palm tree fiber

The present study is an attempt to valorize local vegetable fibers by evaluating thire effectiveness as a new composite biomaterial. Our aim was to determine the mechanical characteristics, namely the elastic modulus and Poisson’s coefficient, of a fiber, locally called “Lif,” that is a natural material extracted from different date palm tree varieties in Biskra, a region situated in southeastern Algeria. This study compares the mechanical characteristcs of the Lif date palm tree fiber with other synthetic and plant fibers studied previously.     

Numerical simulation of the effects of elastic anisotropy and grain size upon the machining of AA2024

Turning modeling and simulation of different metallic materials using the commercially available Finite Element (FE) softwares is getting prime importance because of saving of time and money in comparison to the costly experiments. Mostly, the numerical analysis of machining process considers a purely isotropic behavior of metallic materials; however, the literature shows that the elastic crystal anisotropy is present in most of the ‘so-called’ isotropic materials. In the present work, the elastic anisotropy is incorporated in the FE simulations along with the effect of grain size. A modified Johnson-Cook ductile material model based on coupled plasticity and damage evolution has been proposed to model the cutting process. The simulation results were compared with experimental data on the turning process of Aluminum alloy (AA2024). It was found that the elastic anisotropy influences the average cutting force up to 5% as compared to the isotropic models while the effect of grain size was more pronounced up to 20%.     

Software‐defined networking approach for enhanced evolved packet core network

The evolved packet core (EPC) network is the mobile network standardized by the 3rd Generation Partnership Project and represents the recent evolution of mobile networks providing high‐speed data rates and on‐demand connectivity services. Software‐defined networking (SDN) is recently gaining momentum in network research as a new generation networking technique. An SDN‐based EPC is expected to introduce gains to the EPC control plane architecture in terms of simplified, and perhaps even software‐based, vendor independent infrastructure nodes. In this paper, we propose a novel SDN‐based EPC architecture along with the protocol‐level detailed implementation and provide a mechanism for identifying information fields exchanged between SDN‐EPC entities that maintains correct functionality with minimal impact on the conventional design. Furthermore, we present the first comprehensive network performance evaluation for the SDN‐based EPC versus the conventional EPC and provide a comparative analysis of 2 networks performances identifying potential bottlenecks and performance issues. The evaluation focuses on 2 network control operations, namely, the S1‐handover and registration operations, taking into account several factors, and assessing performance metrics such as end‐to‐end delay (E2ED) for completion of the respective control operation, and EPC nodes utilization figures.     

Lower-bound time-complexity greening mechanism for duplication-based scheduling on large-scale computing platforms

The Journal of Supercomputing - Large-scale computing platforms become essential in nowadays business and scientific activities. The electrical energy consumed by such platforms increases...     

Congestion‐aware multiaccess edge computing collaboration model for 5G

In 5G cloud computing, the most notable and considered design issues are the energy efficiency and delay. The majority of the recent studies were dedicated to optimizing the delay issue by leveraging the edge computing concept, while other studies directed its efforts towards realizing a green cloud by minimizing the energy consumption in the cloud. Active queue management‐based green cloud model (AGCM) as one of the recent green cloud models reduced the delay and energy consumption while maintaining a reliable throughput. Multiaccess edge computing (MEC) was established as a model for the edge computing concept and achieved remarkable enhancement to the delay issue. In this paper, we present a handoff scenario between the two cloud models, AGCM and MEC, to acquire the potential gain of such collaboration and investigate its impact on the cloud fundamental constraints; energy consumption, delay, and throughput. We examined our proposed model with simulation showing great enhancement for the delay, energy consumption, and throughput over either model when employed separately.     

Robust palm and knuckle ROI extraction in unconstrained environment

Pattern Analysis and Applications - Palm and knuckle prints can be extracted from a hand using a low-cost camera in a contactless manner. This makes the process of palm and knuckle recognition fast...     

Feature‐based recursive observer design for homography estimation and its application to image stabilization

This paper presents a new algorithm for online estimation of a sequence of homographies applicable to image sequences obtained from robotic vehicles equipped with vision sensors. The approach taken exploits the underlying Special Linear group structure of the set of homographies along with gyroscope measurements and direct point‐feature correspondences between images to develop temporal filter for the homography estimate. Theoretical analysis and experimental results are provided to demonstrate the robustness of the proposed algorithm. The experimental results show excellent performance and robustness even in the case of very fast camera motions (relative to frame rate) and severe occlusions.     

Stability analysis and saturation control for nonlinear positive Markovian jump systems with randomly occurring actuator faults

This article investigates the stability analysis and control design of a class of nonlinear positive Markovian jump systems with randomly occurring actuator faults and saturation. It is assumed that the actuator faults of each subsystem are varying and governed by a Markovian process. The nonlinear term is located in a sector. First, sufficient conditions for stochastic stability of the underlying systems are established using a stochastic copositive Lyapunov function. Then, a family of reliable L₁‐gain controller is proposed for nonlinear positive Markovian jump systems with actuator faults and saturation in terms of a matrix decomposition technique. Under the designed controllers, the closed‐loop systems are positive and stochastically stable with an L₁‐gain performance. An optimization method is presented to estimate the maximum domain of attraction. Furthermore, the obtained results are developed for general Markovian jump systems. Finally, numerical examples are given to illustrate the effectiveness of the proposed techniques.     

A review on the design and optimization of antennas using machine learning algorithms and techniques

This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.     

2D/3D Shaped Radiation Patterns of Sunflower and Conformal Antenna Arrays Using Phase Synthesis

2D and 3D beam synthesis from different antenna array arrangements are investigated in this paper. Planar sunflower, conformal cylinderical and spherical helical array arrangements are studied. The particle swarm optimization (PSO) technique is used to predict the phase distribution on the array elements. The beam synthesis is achieved by comparing the array factor with a predetermined mask with both upper and lower limits according to the intented application requirements. Different 2D and 3D masks are used in beam synthesis as pencil, flat-top, and cosecant single beam are predicted. The planar sunflower antenna array is investigated due to its high gain, low side-lobe level (SLL) below ??20 dB and its compact size. The phase distribution of sunflower array is estimated using PSO to radiate dual-beams in different planes. Dual-beam with pencil, flat-top, and cosecant beams are obtained with different half-power beam widths. 3D conformal antenna arrays of cylindrical and spherical helical arrangements are studied. Each 3D conformal array consists of four arms shifted in position by 90° orintation angle. Each arm is designed to radiate single beam in a specific direction. Four-beams are considered to radiate in the directions of θ_1,2,3,4?=?30°, and ?₁?=?0°, ?₂?=?90°, ?₃?=?180°, and ?₄?=?270° with SLL optimized below ??17 dB. The array arrangements analysis is based on the array theory formulation, through the implemention of the estimiated equation using a home programmed MATLAB code.