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

The possibility of automatically classifying high frequency sub-bottomacoustic reflections collected from an Autonomous Underwater Robot isinvestigated in this paper. In field surveys of Cobalt-rich Manganese Crusts(Mn-crusts), existing methods relies on visual confirmation of seafloor fromimages and thickness measurements using the sub-bottom probe. Using thesevisual classification results as ground truth, an autoencoder is trained toextract latent features from bundled acoustic reflections. A Support VectorMachine classifier is then trained to classify the latent space to idetifyseafloor classes. Results from data collected from seafloor at 1500m deepregions of Mn-crust showed an accuracy of about 70%.

Seafloor Classification based on an AUV Based Sub-bottom Acoustic Probe Data for Mn-crust survey