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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Applications of machine learning are subject to three major components thatcontribute to the final performance metrics. Within the category of neuralnetworks, and deep learning specifically, the first two are the architecturefor the model being trained and the training approach used. This work focuseson the third component, the data used during training. The primary questionsthat arise are ``what is in the data'' and ``what within the data matters?''Looking into the Radio Frequency Machine Learning (RFML) field of AutomaticModulation Classification (AMC) as an example of a tool used for situationalawareness, the use of synthetic, captured, and augmented data are examined andcompared to provide insights about the quantity and quality of the availabledata necessary to achieve desired performance levels. There are three questionsdiscussed within this work: (1) how useful a synthetically trained system isexpected to be when deployed without considering the environment within thesynthesis, (2) how can augmentation be leveraged within the RFML domain, andlastly, (3) what impact knowledge of degradations to the signal caused by thetransmission channel contributes to the performance of a system. In general,the examined data types each have useful contributions to a final application,but captured data germane to the intended use case will always provide moresignificant information and enable the greatest performance. Despite thebenefit of captured data, the difficulties and costs that arise from livecollection often make the quantity of data needed to achieve peak performanceimpractical. This paper helps quantify the balance between real and syntheticdata, offering concrete examples where training data is parametrically variedin size and source.

Training Data Augmentation for Deep Learning Radio Frequency Systems