Towards van der Waals Epitaxial Growth of GaAs on Si using a Graphene Buffer Layer

Van der Waals growth of GaAs on silicon using a two‐dimensional layered material, graphene, as a lattice mismatch/thermal expansion coefficient mismatch relieving buffer layer is presented. Two‐dimensional growth of GaAs thin films on graphene is a potential route towards heteroepitaxial integration of GaAs on silicon in the developing field of silicon photonics. Hetero‐layered GaAs is deposited by molecular beam epitaxy on graphene/silicon at growth temperatures ranging from 350 °C to 600 °C under a constant arsenic flux. Samples are characterized by plan‐view scanning electron microscopy, atomic force microscopy, Raman microscopy, and X‐ray diffraction. The low energy of the graphene surface and the GaAs/graphene interface is overcome through an optimized growth technique to obtain an atomically smooth low­ temperature GaAs nucleation layer. However, the low adsorption and migration energies of gallium and arsenic atoms on graphene result in cluster‐growth mode during crystallization of GaAs films at an elevated temperature. In this paper, we present the first example of an ultrasmooth morphology for GaAs films with a strong (111) oriented fiber‐texture on graphene/silicon using quasi van der Waals epitaxy, making it a remarkable step towards an eventual demonstration of the epitaxial growth of GaAs by this approach for heterogeneous integration.     

The XFEM for high‐gradient solutions in convection‐dominated problems

Convection‐dominated problems typically involve solutions with high gradients near the domain boundaries (boundary layers) or inside the domain (shocks). The approximation of such solutions by means of the standard finite element method requires stabilization in order to avoid spurious oscillations. However, accurate results may still require a mesh refinement near the high gradients. Herein, we investigate the extended finite element method (XFEM) with a new enrichment scheme that enables highly accurate results without stabilization or mesh refinement. A set of regularized Heaviside functions is used for the enrichment in the vicinity of the high gradients. Different linear and non‐linear problems in one and two dimensions are considered and show the ability of the proposed enrichment to capture arbitrary high gradients in the solutions. Copyright © 2009 John Wiley & Sons, Ltd.     

Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions

Automatic deception recognition has received considerable attention from the machine learning community due to recent research on its vast application to social media, interviews, law enforcement, and the military. Video analysis-based techniques for automated deception detection have received increasing interest. This study develops a new self-adaptive population-based firefly algorithm with a deep learning-enabled automated deception detection (SAPFF-DLADD) model for analyzing facial cues. Initially, the input video is separated into a set of video frames. Then, the SAPFF-DLADD model applies the MobileNet-based feature extractor to produce a useful set of features. The long short-term memory (LSTM) model is exploited for deception detection and classification. In the final stage, the SAPFF technique is applied to optimally alter the hyperparameter values of the LSTM model, showing the novelty of the work. The experimental validation of the SAPFF-DLADD model is tested using the Miami University Deception Detection Database (MU3D), a database comprised of two classes, namely, truth and deception. An extensive comparative analysis reported a better performance of the SAPFF-DLADD model compared to recent approaches, with a higher accuracy of 99%.     

An investigation and comparison of machine learning approaches for intrusion detection in IoMT network

The Journal of Supercomputing - Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications,...