Abstract: A typical computer networking class curriculum includes the study of theory and the laboratory projects. Creating engaging laboratory and classroom experiences is a challenge to effective ...

Abstract: Nowadays, big data technology has been frequently used in computer network information management. Big data technology can make computer network information management more convenient and ...

Abstract: This study aims to enhance the accuracy and generalization of motor imagery-based brain-computer interface (MI-BCI) systems using a novel frequency-based graph convolutional neural network ...

Abstract: Decomposition is a fundamental principle of resolving complexity by scale, which is utilized in a variety of decomposition-based algorithms for control and optimization. In this paper, we ...

Abstract: Flooding topology is a subset of initial network's links, which can be used by flooding algorithms to send broadcast messages. Use of such topology can significantly reduce network load.

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

Abstract: This tutorial brief shows how Artificial Neural Networks (ANNs) can be used for the optimization and automated design of analog and mixed-signal circuits. A survey of conventional and ...

Learn how to create contour plots in Python using NumPy’s meshgrid and Matplotlib. This step-by-step tutorial shows you how to generate grids, compute functions over them, and visualize data ...

Abstract: Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is ...

Abstract: Histopathology diagnosis is an important standard for breast tumors identifying. However, histopathology image analysis is complex, tedious, and error-prone, due to the super-resolution ...