Through NETCOM’s journal-first initiative, authors of journal-first papers accepted in the following journals are invited to present their research work to the wider audiences. It is providing an opportunity for the authors to engage directly with the conference authors and offering an additional dimension to the research track program.
Submission Deadline : April 23, 2022
Authors Notification : May 23, 2022
The journal-first manuscripts will not be part of the NETCOM conference proceedings.
Rohini Saxena, Mukesh Kumar and Shadman Aslam, University of Allahabad, India
In this paper, a novel Evolutionary Computing named Adaptive Genetic Algorithm (AGA) based ANN model is developed for rectangular MPA (Microstrip patch antenna). Considering at-hand and Nextgeneration Ultra wideband application demands, the emphasis has been made on retaining optimal lowcost design with desired cut-off frequency. The proposed method employs multiple sets of theoreticallydriven training instances or patch antenna design parameters which have been processed for normalization and sub-sampling to achieve a justifiable and reliable sample size for further design parameter prediction. Procedurally, the input design parameters were processed for normalization followed by sub-sampling to give rise to a sufficient set of inputs to perform knowledge-driven (designparameter) prediction. Considering limitations of the major at-hand machine learning methods which often undergo local minima and convergence while training, we designed a state-of-art new Adaptive Genetic Algorithm based neuro-computing model (AGA-ANN), which helped to predict the set of optimal design parameters for rectangular microstrip patch antenna. The predicted patch antenna length and width values were later used for verification which achieved the expected frequency. The depth analysis revealed that a rectangular patch antenna with width 14.78 mm, length 11.08mm, feed-line 50 Ω can achieve the cut-off frequency of 8.273 GHz, which can be of great significance for numerous UWB applications.
Microstrip Patch Antenna, Design Optimization, Evolutionary Computing, Neural Network, Ultra-Wideband.
Luz Angela Aristizábal Q and Nicolás Toro G, Universidad Nacional de Colombia, Colombia
The constant increase in the complexity of data networks motivates the search for strategies that make it possible to reduce current monitoring times. This paper shows the way in which multilayer network representation and the application of multiscale analysis techniques, as applied to software-defined networks, allows for the visualization of anomalies from "coarse views of the network topology". This implies the analysis of fewer data, and consequently the reduction of the time that a process takes to monitor the network. The fact that software-defined networks allow for the obtention of a global view of network behavior facilitates detail recovery from affected zones detected in monitoring processes. The method is evaluated by calculating the reduction factor of nodes, checked during anomaly detection, with respect to the total number of nodes in the network.
Multiscale analysis, Multilayer representation, Graph signal processing, Software defined networks, Monitoring.
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