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 lack of adequate training data is one of the major hurdles in WiFi-basedactivity recognition systems. In this paper, we propose Wi-Fringe, which is aWiFi CSI-based device-free human gesture recognition system that recognizesnamed gestures, i.e., activities and gestures that have a semanticallymeaningful name in English language, as opposed to arbitrary free-formgestures. Given a list of activities (only their names in English text), alongwith zero or more training examples (WiFi CSI values) per activity, Wi-Fringeis able to detect all activities at runtime. In other words, a subset ofactivities that Wi-Fringe detects do not require any training examples at all.

Wi-Fringe: Leveraging Text Semantics in WiFi CSI-Based Device-Free Named Gesture Recognition