Analyzing Radio Signals

Feb 26, 2021 ... We developed a context-aware graph neural network (GNN) model utilizing the pairwise relationship among participants to investigate the ... The human brain is often used as an analogy to explain neural networks, however this is not particularly helpful as it implies machines understand information ... Sep 4, 2020 ... TABOO: De- tecting unstructured sensitive information using recursive neural networks. In Proceedings of the 33rd International Conference on. ... neural networks contribute to familiar face recognition. Original language, English. Article number, 117896. Pages (from-to), 1-15. Number of pages, 15. Journal ... Feb 7, 2018 ... ... information than excitatory connections, known to enhance entropy in the network. ... Neural information · Neuronal dynamics · Topological tools ... We propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma ... Sep 22, 2020 ... By recruiting adjacent neurons into oscillations, waves of synchronously firing neuronal networks—and thereby conscious thoughts—can travel ... May 1, 2020 ... This review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals. He also gives an example using a neural network class with fixed architecture as the hypothesis class. He is able to predict the qualitative effects of ... Data-driven network performance prediction for B5G networks: a graph neural network approach. Yaqoob, M., Trestian, R. and Nguyen, H. 2022. Data-driven ...