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

Recently, Wi-Fi has caught tremendous attention for its ubiquity, and,motivated by Wi-Fi's low cost and privacy preservation, researchers have beenputting lots of investigation into its potential on action recognition and evenperson identification. In this paper, we offer an comprehensive overview onthese two topics in Wi-Fi. Also, through looking at these two topics from anunprecedented perspective, we could achieve generality instead of designingspecific ad-hoc features for each scenario. Observing the great resemblance ofChannel State Information (CSI, a fine-grained information captured from thereceived Wi-Fi signal) to texture, we proposed a brand-new framework based oncomputer vision methods. To minimize the effect of location dependency embeddedin CSI, we propose a novel de-noising method based on Singular ValueDecomposition (SVD) to eliminate the background energy and effectively extractthe channel information of signals reflected by human bodies. From theexperiments conducted, we demonstrate the feasibility and efficacy of theproposed methods. Also, we conclude factors that would affect the performanceand highlight a few promising issues that require further deliberation.

We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based Methods