Dual protocol performance using WiFi and ZigBee for industrial WLAN

The purpose of this thesis is to study the performance of a WNCS based on utilizing IEEE 802.15.4 and IEEE 802.11 in meeting industrial requirements as well as the extent of improvement on the network level in terms of latency and interference tolerance when using the two different protocols, namely WiFi and ZigBee, in parallel. The study evaluates the optimum performance of WNCS that utilizes only IEEE 802.15.4 protocol (which ZigBee is based on) without modifications as an alternative that is low cost and low power compared to other wireless technologies. The study also evaluates the optimum performance of WNCS that utilizes only the IEEE 802.11 protocol (WiFi) without modifications as a high bit network. OMNeT++ simulations are used to measure the end-to-end delay and packet loss from the sensors to the controller and from the controller to the actuators. It is demonstrated that the measured delay of the proposed WNCS including all types of transmission, encapsulation, de-capsulation, queuing and propagation, meet real-time control network requirements while guaranteeing correct packet reception with no packet loss. Moreover, it is shown that the demonstrated performance of the proposed WNCS operating redundantly on both networks in parallel is significantly superior to a WNCS operating on either a totally wireless ZigBee or WiFi network individually in terms of measured delay and interference tolerance. This proposed WNCS demonstrates the combined advantages of both the IEEE 802.15.4 protocol (which ZigBee is based on) without modifications being low cost and low power compared to other wireless technologies as well the advantages of the IEEE 802.11 protocol (WiFi) being increased bit rate and higher immunity to interference. All results presented in this study were based on a 95% confidence analysis

The Implementation of an Integrated Management System at Qatar Biobank

Qatar Biobank (QBB) is a platform that will make vital health research possible through its collection of samples and information on health and lifestyle from the local population of Qatar. The goal of QBB is to collect, process, store, and finally share high-quality biological samples and associated data for research purposes with the research community. To do this, a series of standardized procedures following evidence-based practices are required, and QBB is achieving this by implementing an integrated management system (IMS) that incorporates ISO 9001: 2015 and ISO 27001: 2013 standards. ISO 9001 is one of the most commonly implemented quality management systems as it is applicable to any size of organization. ISO 27001: 2013 is increasingly popular as organizations look to manage their data and information security, especially in the light of the recent General Data Protection Regulation legislation and an ever-changing digital landscape. QBB has achieved certification in both ISO 9001: 2015 (originally 2008 standard) and ISO 27001: 2013 since 2014. In 2016, during preparations for recertification of both standards in 2017, QBB chose to integrate both of the management systems in preference to running them in parallel, without compromising the goals and objectives of QBB. The IMS has ensured that rigorous processes and controls are implemented to not only manage the quality of internal and external processes and services provided, but the privacy and confidentiality of data collected during a participant visit are consistently protected as well as a proactive approach to identifying and managing risk within the organization. This article will explore the impact of implementing an IMS on the continuous improvement of services within QBB

Cellular Network-Supported Machine Learning Techniques for Autonomous UAV Trajectory Planning

Autonomous trajectory planning is a hot topic in the UAV mission planning area of research. Autonomous UAVs have major use case applications which involve navigation in complex environments such as aerial photography, package delivery and relief operations. Many existing trajectory planning solutions rely on the GPS system. However, such GPS-based solutions do not provide a reliable real-time navigation solution, particularly in dense urban environments. Opportunely, cellular networks can be utilized as an attractive alternative for UAV navigation applications. We therefore propose to utilize existing 5G infrastructure to enable the UAV to navigate complex environments, independent of the GPS and other detectable signals transmitted by the UAV. Our objective is to propose an efficient solution to enable the UAVs to autonomously execute such tasks while meeting the real-time operational requirements, without the need to actually interact with the cellular network. For this purpose, we formulate the UAV trajectory planning problem as a joint objective optimization problem to minimize a composite cost metric that we introduce. The computational complexity involved in exact optimization techniques hinders obtaining the real-time calculation requirement that is needed due to the dynamic nature of the environment. To overcome this complexity, we utilize machine learning based techniques to solve the formulated trajectory planning problem. Specifically, we propose two machine learning-based techniques, namely, the reinforcement learning and the deep supervised learning-based approaches. We then analyze the performance of each of the proposed techniques as compared to the optimization-based approaches and other solutions from the literature. Our simulation results show that the proposed reinforcement and deep supervised learning-based solutions provide near optimal solutions to the formulated trajectory planning problem, with comparable accuracy of 99% and 98%, respectively, as compared to the optimal bound while meeting the real-time calculation requirement

Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches

Unmanned aerial vehicles (UAVs) are becoming an integral part of numerous commercial and military applications. In many of these applications, the UAV is required to self-navigate in highly dynamic urban environments. This means that the UAV must have the ability to determine its location in an autonomous and real time manner. Existing localization techniques rely mainly on the Global Positioning System (GPS) and do not provide a reliable real time localization solution, particularly in dense urban environments. Our objective is to propose an effective alternative solution to enable the UAV to autonomously determine its location independent of the GPS and without message exchanges. We therefore propose utilizing the existing 5G cellular infrastructure to enable the UAV to determine its 3-D location without the need to interact with the cellular network. We formulate the UAV localization problem to minimize the error of the RSSI measurements from the surrounding cellular base stations. While exact optimization techniques can be applied to accurately solve such a problem, they cannot provide the real time calculation that is needed in such dynamic applications. Machine learning based techniques are strong candidates to provide an attractive alternative to provide a near-optimal localization solution with the needed practical real-time calculation. Accordingly, we propose two machine learning-based approaches, namely, deep neural network and reinforcement learning based approaches, to solve the formulated UAV localization problem in real time. We then provide a detailed comparative analysis for each of the proposed localization techniques along with a comparison with the optimization-based techniques as well as other techniques from the literature

Dual protocol performance using WiFi and Zigbee for industrial WLAN

[abstract not available

Increased arterial stiffness in rheumatoid arthritis and Its relation to disease activity: A cross sectional study

Background: Rheumatoid arthritis (RA) is associated with elevated plasma level of inflammatory markers. Chronic inflammation is known to predispose to endothelial dysfunction and increased arterial stiffness, which is an important marker of subclinical atherosclerosis and increased cardiovascular risk. Objective: The aim is to test for the relationship between disease activity and arterial stiffness in RA patients. Methods: The study included 90 RA patients, at different grades of disease activity and 45 healthy subjects, as a control group. Patients were subjected to full history taking and clinical examination, laboratory investigations including serum lipid profile and high sensitivity CRP (hs-CRP) measurements and plain x-rays of hands and feet. Modified Larsen method was used as radiographic scoring method. Disease activity score (DAS 28) was used for assessment of disease activity. Transthoracic echocardiography was performed to detect aortic stiffness parameters. Duplex ultrasound imaging of both common carotid arteries was performed to measure carotid stiffness parameters. Results: The mean age of RA patients was 39.86 ± 9.39 years and most of them (83.3%) were females. RA patients had higher carotid stiffness index compared to control group patients (8.57 ± 4.83 vs 4.08 ± 1.13, p < .001). Very poor correlation was found between DAS-28 and aortic (r = 0.1, p = .28) as well as carotid (r = 0.05, p = .7) stiffness indices. No statistically significant correlation was found between hs-CRP and aortic stiffness index (r = 0.64, p = .55). Disease duration was significantly correlated to intima-media thickness (p < .01) as well as with other carotid stiffness parameters. Age also show a statistically significant positive correlation with carotid stiffness parameters. Conclusion: RA is associated with increased arterial stiffness, a well-recognized marker of cardiovascular risk. This is attributed to the inflammatory nature of the disease. It seems that the most important factors determining stiffness are patients' age and duration of illness. Keywords: RA, hs-CRP, Arterial stiffnes

A ZigBee-based industrial WLAN

[abstract not available

The supposed tumor suppressor gene WWOX is mutated in an early lethal microcephaly syndrome with epilepsy, growth retardation and retinal degeneration

Background: WWOX, encoding WW domain-containing oxidoreductase, spans FRA16D, the second most common chromosomal fragile site frequently altered in cancers. It is therefore considered a tumor suppressor gene, but its direct implication in cancerogenesis remains controversial. Methods and results: By whole-exome sequencing, we identified a homozygous WWOX nonsense mutation, p.Arg54*, in a girl from a consanguineous family with a severe syndrome of growth retardation, microcephaly, epileptic seizures, retinopathy and early death, a phenotype highly similar to the abormalities reported in lde/lde rats with a spontaneous functional null mutation of Wwox. As in rats, no tumors were observed in the patient or heterozygous mutation carriers. Conclusions: Our finding, a homozygous loss-of-function germline mutation in WWOX in a patient with a lethal autosomal recessive syndrome, supports an alternative role of WWOX and indicates its importance for human viability