Throughput Maximization of Cognitive Radio Multi Relay Network with Interference Management

In this paper, an Orthogonal Frequency Division Multiplexing (OFDM) based cognitive multi relay network is investigated to maximize the transmission rate of the cognitive radio (CR) with enhanced  fairness among CR users  with interference to the primary users (PUs) being managed below a certain threshold level. In order to improve the transmission rate of the CR, optimization of the subcarrier pairing and power allocation is to be carried out simultaneously. Firstly joint optimization problem is formulated and Composite Genetic and Ordered Subcarrier Pairing (CGOSP) algorithm is proposed to solve the problem. The motivation behind merging genetic and OSP algorithm is to reduce the complexity of Genetic Algorithm (GA). Further, to have a fair allocation of resources among CR users, the Round Robin allocation method is adopted so as to allocate subcarrier pairs to relays efficiently. The degree of fairness of the system is calculated using Jain’s Fairness Index (JFI). Simulation results demonstrate the significant improvement in transmission rate of the CR, low computational complexity and enhanced fairness

PERFORMANCE ANALYSIS OF IPv4 AND IPv6 INTERNET TRAFFIC

The gigantic growth of the internet communication technology has illustrated its value and benefits to private businesses, government organizations, worldwide professionals, academic institutes and individuals over the past few years. The size and range of computing devices connected to the internet, substantially increased because of IPv6 and offers the potential to establish a much more powerful internet compared to the IPv4. IPV6 developed by the IETF to deal with a shortage of IP addresses under IPv4. New features of IPv6 enhance packet processing speeds over routers, switches and end systems. These improved features will have different traffic characteristics than IPv4. The internet traffic which was earlier assumed as Poisson is now shown to have fractal characteristics as; heavy tailedness, self-similarity and long range dependency. Internet traffic showing above characteristics are found to have burstiness at multiple timescales. This behavior impacts network performance and degrades it substantially. It also increases complexity for network design and create difficulties to maintain desired QoS. IPv4 traffic has been well established as self-similar traffic. Nowadays, IPv6 forming a larger share of the internet traffic and it is pivotal to asses IPv6 with regards to fractal behavior. This will enable network designers to do necessary changes in the existing network to reconcile with IPv6. In this paper we compared IPv4 and IPv6 with respect to fractal behavioral characteristics. It is found that IPv6 shows higher degree of heavy tailedness, higher values of Hurst parameter values, higher fractal dimension values i.e. it is more self-similar, greater autocorrelation achieved even at larger lag and thus showing more burstiness

A NOVEL APPROACH FOR REAL TIME INTERNET TRAFFIC CLASSIFICATION

Real time internet traffic classification is imperative for service discrimination, network security and network monitoring. Classification of traffic depends on initial first few network packets of full flows of captured IP traffic. Practically, the real world framework situation expects correct conclusion of classification well before a flow has ended even if the start of the Traffic flow is missed. This is achieved by calculating features from few N network packets, taken at any random time instant at any random point in the duration of flow. This research proposes a novel parameter Relative Uncertainty (RU) to estimate the level of diversity of internet traffic and can then be used for characterization of internet traffic. Small sub-flows from Full-flows are selected based on minimum RU value (MRUB-SFs: Minimum RU Based Sub Flows), and then features are calculated for training the C4.5 ML classifier. Experimentation is carried out with various standard datasets and results stable accuracy of 99.3167% for different classes of applications

Performance Analysis of Two Receiver Arrangements for Wireless Battery Charging System

Two different arrangements for Wireless Battery Charging Systems (WBCSs) with a series-parallel resonant topology have been analyzed in this paper. The first arrangement charges the battery by controlling the receiver-side rectifier current and voltage without a chopper, while the second arrangement charges it with a chopper while keeping the chopper input voltage constant. The comparison of these two arrangements is made based on their performance on various figures of merit, such as the sizing factor of both the supply voltage source and receiver coil, overall system efficiency, power-transfer ratio, receiver efficiency, and cost estimation. Later, the simulated study is verified by the experimental setup designed to charge the electric vehicle

Modeling of the Resonant Inverter for Wireless Power Transfer Systems Using the Novel MVLT Method

Wireless power transfer (WPT) is a power transfer technique widely used in many industrial applications, medical applications, and electric vehicles (EVs). This paper deals with the dynamic modeling of the resonant inverter employed in the WPT systems for EVs. To this end, the Generalized State-Space Averaging and the Laplace Phasor Transform techniques have been the flagship methods employed so far. In this paper, the modeling of the resonant inverter is accomplished by using the novel Modulated Variable Laplace Transform (MVLT) method. Firstly, the MVLT technique is discussed in detail, and then it is applied to model a study-case resonant inverter. Finally, a study-case resonant inverter is developed and utilized to validate the theoretical results with MATLAB/Simulink

Ocular surface manifestations of coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis.

PurposeThis study was performed to determine the occurrence of ocular surface manifestations in patients diagnosed with coronavirus disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).MethodsA systematic search of electronic databases i.e. PubMed, Web of Science, CINAHL, OVID and Google scholar was performed using a comprehensive search strategy. The searches were current through 31st May 2020. Pooled data from cross-sectional studies was used for meta-analysis and a narrative synthesis was conducted for studies where a meta-analysis was not feasible.ResultsA total of 16 studies reporting 2347 confirmed COVID-19 cases were included. Pooled data showed that 11.64% of COVID-19 patients had ocular surface manifestations. Ocular pain (31.2%), discharge (19.2%), redness (10.8%), and follicular conjunctivitis (7.7%) were the main features. 6.9% patients with ocular manifestations had severe pneumonia. Viral RNA was detected from the ocular specimens in 3.5% patients.ConclusionThe most common reported ocular presentations of COVID-19 included ocular pain, redness, discharge, and follicular conjunctivitis. A small proportion of patients had viral RNA in their conjunctival/tear samples. The available studies show significant publication bias and heterogeneity. Prospective studies with methodical collection and data reporting are needed for evaluation of ocular involvement in COVID-19

Antiviral Therapy for Varicella Zoster Virus (VZV) and Herpes Simplex Virus (HSV)-Induced Anterior Uveitis: A Systematic Review and Meta-Analysis

10.3389/fmed.2021.686427Frontiers in Medicine868642

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Not AvailableMango is one of the most important fruits of tropical ecological region of the world, well known for its nutritive value, aroma and taste. Its world production is >45MT worth >200 billion US dollars. Genomic resources are required for improvement in productivity and management of mango germplasm. There is no web-based genomic resources available for mango. Hence rapid and cost-effective high throughput putative marker discovery is required to develop such resources. RAD-based marker discovery can cater this urgent need till whole genome sequence of mango becomes available. Using a panel of 84 mango varieties, a total of 28.6 Gb data was generated by ddRAD-Seq approach on Illumina HiSeq 2000 platform. A total of 1.25 million SNPs were discovered. Phylogenetic tree using 749 common SNPs across these varieties revealed three major lineages which was compared with geographical locations. A web genomic resources MiSNPDb, available at http://webtom.cabgrid.res.in/mangosnps/ is based on 3-tier architecture, developed using PHP, MySQL and Javascript. This web genomic resources can be of immense use in the development of high density linkage map, QTL discovery, varietal differentiation, traceability, genome finishing and SNP chip development for future GWAS in genomic selection program. We report here world’s first web-based genomic resources for genetic improvement and germplasm management of mango.Not Availabl