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

Extracting information from radiofrequency (RF) signals using artificialneural networks at low energy cost is a critical need for a wide range ofapplications from radars to health. These RF inputs are composed of multiplesfrequencies. Here we show that magnetic tunnel junctions can process analogueRF inputs with multiple frequencies in parallel and perform synapticoperations. Using a backpropagation-free method called extreme learning, weclassify noisy images encoded by RF signals, using experimental data frommagnetic tunnel junctions functioning as both synapses and neurons. We achievethe same accuracy as an equivalent software neural network. These results are akey step for embedded radiofrequency artificial intelligence.

Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses