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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Slow feature analysis (SFA) is a method for extracting slowly varying drivingforces from quickly varying nonstationary time series. We show here that it ispossible for SFA to detect a component which is even slower than the drivingforce itself (e.g. the envelope of a modulated sine wave). It is shown that itdepends on circumstances like the embedding dimension, the time seriespredictability, or the base frequency, whether the driving force itself or aslower subcomponent is detected. We observe a phase transition from one regimeto the other and it is the purpose of this work to quantify the influence ofvarious parameters on this phase transition. We conclude that what is percievedas slow by SFA varies and that a more or less fast switching from one regime tothe other occurs, perhaps showing some similarity to human perception.

How slow is slow? SFA detects signals that are slower than the driving force