Study of magnetic properties and characterization of Ni/Cu multi-layer structures using in-situ high-temperature X-Ray diffraction (HT-XRD)

In this paper, the results of magnetic and structural measurements of Ni/Cu multi-layers with different thicknesses of Cu and Ni grown on Si (100) substrate are presented. The Ni/Cu multi-layers nanostructure is analyzed using X-ray diffraction (XRD) diagram at room temperature and X-ray diffraction in a vacuum at high temperature (HT-XRD). The periodic peaks in the XRD spectrum, the multilayer structure and thus, the superlattice structure of the films are confirmed. But the in-situ XRD spectrums at high temperature reveal that the increase of temperature and annealing time is associated with the decrease or disappearance of intensity of the multilayer peaks and periodic peaks. It means the increase of these two parameters is accompanied with the weakening of interface sharpness between the layers, and the samples' multilayer structures are inclined toward the alloy structure

Study of magnetic properties and characterization of Ni/Cu multi-layer structures using in-situ high-temperature X-Ray diffraction (HT-XRD)

In this paper, the results of magnetic and structural measurements of Ni/Cu multi-layers with different thicknesses of Cu and Ni grown on Si (100) substrate are presented. The Ni/Cu multi-layers nanostructure is analyzed using X-ray diffraction (XRD) diagram at room temperature and X-ray diffraction in a vacuum at high temperature (HT-XRD). The periodic peaks in the XRD spectrum, the multilayer structure and thus, the superlattice structure of the films are confirmed. But the in-situ XRD spectrums at high temperature reveal that the increase of temperature and annealing time is associated with the decrease or disappearance of intensity of the multilayer peaks and periodic peaks. It means the increase of these two parameters is accompanied with the weakening of interface sharpness between the layers, and the samples' multilayer structures are inclined toward the alloy structure

Analysis of AeroMACS Data Link for Unmanned Aircraft Vehicles

Aeronautical Mobile Airport Communications System (AeroMACS) is based on the IEEE 802.16e mobile wireless standard commonly known as WiMAX. It is expected to be the main part of the next-generation aviation communication system to support fixed and mobile services for manned and unmanned applications. AeroMACS will be an essential technology helping pave the way toward full integration of Unmanned Aircraft Vehicle (UAV) into the national airspace. A number of practical tests and analyses have been done so far for AeroMACS. The main contribution of this paper is to consider the theoretical concepts behind its features and discuss their suitability for UAV applications. Mathematical analyses of the AeroMACS physical layer framework are provided to show the theoretical trade-offs. We mainly focus on the analysis of the AeroMACS OFDMA structure, which affects the speed limits, coverage cell, channel estimation requirements, and inter-carrier interference

Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using machine learning models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a machine learning based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods

Efficient virtual network function placement strategies for Cloud Radio Access Networks

The new generation of 5G mobile services place stringent requirements for cellular network operators in terms of latency and costs. The latest trend in radio access networks (RANs) is to pool the baseband units (BBUs) of multiple radio base stations and to install them in a centralized infrastructure, such as a cloud, for statistical multiplexing gains. The technology is known as Cloud Radio Access Network (CRAN). Since cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in the telecommunication industry, CRAN is being heralded as a candidate technology to meet the expectations of radio access networks for 5G. In CRANs, low energy base stations (BSs) are deployed over a small geographical location and are connected to a cloud via finite capacity backhaul links. Baseband processing unit (BBU) functions are implemented on the virtual machines (VMs) in the cloud over commodity hardware. Such functions, built in software, are termed as virtual functions (VFs). The optimized placement of VFs is necessary to reduce the total delays and minimize the overall costs to operate CRANs. Our study considers the problem of optimal VF placement over distributed virtual resources spread across multiple clouds, creating a centralized BBU cloud. We propose a combinatorial optimization model and the use of two heuristic approaches, which are, branch-and-bound (BnB) and simulated annealing (SA) for the proposed optimal placement. In addition, we propose enhancements to the standard BnB heuristic and compare the results with standard BnB and SA approaches. The proposed enhancements improve the quality of the solution in terms of latency and cost as well as reduce the execution complexity significantly. We also determine the optimal number of clouds, which need to be deployed so that the total links delays, as well as the service migration delays, are minimized, while the total cloud deployment cost is within the acceptable limits.This publication was made possible by the NPRP award [ NPRP 8-634-1-131 ] from the Qatar National Research Fund (a member of The Qatar Foundation) and NSF Grant CNS-1718929 . The statements made herein are solely the responsibility of the author[ s ]

Green biosynthesis of silver nanoparticles by Spirulina platensis

Abstract Crystallized silver nanoparticles (SNPs) have been biosynthesized by Spirulina platensis in an aqueous system. An aqueous solution of silver ions was treated with a live biomass of Spirulina platensis for the formation of SNPs. These nanoparticles showed an absorption peak at 430 nm in the UV-visible spectrum, corresponding to the plasmon resonance of SNPs. The transmission electron micrographs of nanoparticles in an aqueous solution showed the production of SNPs (average size of most particles: ∼12 nm) by Spirulina platensis. The X-Ray Diffraction (XRD) spectrum of the nanoparticles confirmed the formation of metallic silver, and the average size of the crystallite was estimated from the peak profile by the Scherrer method. The synthesized SNPs had an average size of 11.6 nm

Fault and performance management in multi-cloud virtual network services using AI: A tutorial and a case study

Carriers find Network Function Virtualization (NFV) and multi-cloud computing a potent combination for deploying their network services. The resulting virtual network services (VNS) offer great flexibility and cost advantages to them. However, vesting such services with a level of performance and availability akin to traditional networks has proved to be a difficult problem for academics and practitioners alike. There are a number of reasons for this complexity. The challenging nature of management of fault and performance issues of NFV and multi-cloud based VNSs is an important reason. Rule-based techniques that are used in the traditional physical networks do not work well in the virtual environments. Fortunately, machine and deep learning techniques of Artificial Intelligence (AI) are proving to be effective in this scenario. The main objective of this tutorial is to understand how AI-based techniques can help in fault detection and localization to take such services closer to the performance and availability of the traditional networks. A case study, based on our work in this area, has been included for a better understanding of the concepts. - 2019This publication was made possible by NPRP grant #8-634-1-131 from the Qatar National Research Fund (a member of Qatar Foundation), NSF grants CNS-1718929and CNS-1547380.The statements made herein are solely the responsibility of the authors.This paper draws from earlier works of the authors including ̳HYPER-VINES: A Hybrid Learning Fault and Performance Issues Eradicator for Virtual Network Services over Multi-Cloud Systems’ presented at the IEEE ICNC 2019 Conference in February 2019, andfrom the other references listed in the reference section.Scopu

Cybersecurity for industrial control systems: A survey

Industrial Control System (ICS) is a general term that includes supervisory control & data acquisition (SCADA) systems, distributed control systems (DCS), and other control system configurations such as programmable logic controllers (PLC). ICSs are often found in the industrial sectors and critical infrastructures, such as nuclear and thermal plants, water treatment facilities, power generation, heavy industries, and distribution systems. Though ICSs were kept isolated from the Internet for so long, significant achievable business benefits are driving a convergence between ICSs and the Internet as well as information technology (IT) environments, such as cloud computing. As a result, ICSs have been exposed to the attack vectors used in the majority of cyber-attacks. However, ICS devices are inherently much less secure against such advanced attack scenarios. A compromise to ICS can lead to enormous physical damage and danger to human lives. In this work, we have a close look at the shift of the ICS from stand-alone systems to cloud-based environments. Then we discuss the major works, from industry and academia towards the development of the secure ICSs, especially applicability of the machine learning techniques for the ICS cyber-security. The work may help to address the challenges of securing industrial processes, particularly while migrating them to the cloud environments.American Association for the Advancement of Science; IEEE Foundation; Qatar Foundation; Qatar National Research Fund; Anacostia Community Museum; Academy of Science of St. LouisScopu

Flow-based intrusion detection algorithm for supervisory control and data acquisition systems: A real-time approach

Intrusion detection in supervisory control and data acquisition (SCADA) systems is integral because of the critical roles of these systems in industries. However, available approaches in the literature lack representative flow-based datasets and reliable real-time adaption and evaluation. A publicly available labelled dataset to support flow-based intrusion detection research specific to SCADA systems is presented. Cyberattacks were carried out against our SCADA system test bed to generate this flow-based dataset. Moreover, a flow-based intrusion detection system (IDS) is developed for SCADA systems using a deep learning algorithm. We used the dataset to develop this IDS model for real-time operations of SCADA systems to detect attacks momentarily after they happen. The results show empirical proof of the model’s adequacy when deployed online to detect cyberattacks in real timeNational Science Foundation; Washington University in St. Louis;?Qatar National Research Fund; Funda o de Amparo Pesquisa do Estado de So Paulo; Qatar UniversityScopu

An Integrated Octree-RANSAC Technique for Automated LiDAR Building Data Segmentation for Decorative Buildings

The 12th International Symposium on Visual Computing (ISVC 2016), Las Vegas, United States of America, 12-14 December 2016This paper introduces a new method for the automated segmentation of laser scanning data for decorative urban buildings. The method combines octree indexing and RANSAC - two previously established but heretofore not integrated techniques. The approach was successfully applied to terrestrial point clouds of the facades of five highly decorative urban structures for which existing approaches could not provide an automated pipeline. The segmentation technique was relatively efficient and wholly scalable requiring only 1 second per 1,000 points, regardless of the façade’s level of ornamentation or non-recti-linearity. While the technique struggled with shallow protrusions, its ability to process a wide range of building types and opening shapes with data densities as low as 400 pts/m2 demonstrate its inherent potential as part of a large and more sophisticated processing approach.European Research Counci