English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This work presents the first applications of self-supervised learning appliedto data from digital antenna arrays. Encoder-decoder networks are pretrained ondigital array data to perform a self-supervised noisy-reconstruction taskcalled channel in-painting, in which the network infers the contents of arraydata that has been masked with zeros. The self-supervised step requires nohuman-labeled data. The encoder architecture and weights from pretraining arethen transferred to a new network with a task-specific decoder, and the newnetwork is trained on a small volume of labeled data. We show that pretrainingon the unlabeled data allows the new network to perform the task of bandwidthregression on the digital array data better than an equivalent network that istrained on the same labeled data from random initialization.

Encoder-Decoder Networks for Self-Supervised Pretraining and Downstream Signal Bandwidth Regression on Digital Antenna Arrays