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Reconfigurable Array Control Via Convolutional Neural Networks, Garrett A. Harris
Reconfigurable Array Control Via Convolutional Neural Networks, Garrett A. Harris
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A method for the beam forming control of an array of reconfigurable antennas is presented. The method consists of using two parallel convolutional neural networks (CNNs) to analyze a desired radiation pattern image, or mask, and provide a suggestion for the reconfigurable element state, array shape, and steering weights necessary to obtain the radiation pattern. This research compares beam forming systems designed for three distinct element types: a patch antenna, a reconfigurable square spiral antenna restricted to a single reconfigurable state, and the fully reconfigurable square spiral. The parametric sweeps for the design of the CNNs are presented along with …
Online Clustering With Bayesian Nonparametrics, Matthew D. Scherreik
Online Clustering With Bayesian Nonparametrics, Matthew D. Scherreik
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Clustering algorithms, such as Gaussian mixture models and K-means, often require the number of clusters to be specified a priori. Bayesian nonparametric (BNP) methods avoid this problem by specifying a prior distribution over the cluster assignments that allows the number of clusters to be inferred from the data. This can be especially useful for online clustering tasks, where data arrives in a continuous stream and the number of clusters may dynamically change over time. Classical BNP priors often overestimate the number of clusters, however, leading researchers to develop new priors with more control over this tendency. To date, BNP algorithms …