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

Non-stationary signals are ubiquitous in real life. Many techniques have beenproposed in the last decades which allow decomposing multi-component signalsinto simple oscillatory mono-components, like the groundbreaking Empirical ModeDecomposition technique and the Iterative Filtering method. When a signalcontains mono-components that have rapid varying instantaneous frequencies, wecan think, for instance, to chirps or whistles, it becomes particularly hardfor most techniques to properly factor out these components. The Adaptive LocalIterative Filtering technique has recently gained interest in many appliedfields of research for being able to deal with non-stationary signalspresenting amplitude and frequency modulation. In this work, we address theopen question of how to guarantee a priori convergence of this technique, andpropose two new algorithms. The first method, called Stable Adaptive LocalIterative Filtering, is a stabilized version of the Adaptive Local IterativeFiltering that we prove to be always convergent. The stability, however, comesat the cost of higher complexity in the calculations. The second technique,called Resampled Iterative Filtering, is a new generalization of the IterativeFiltering method. We prove that Resampled Iterative Filtering is guaranteed toconverge a priori for any kind of signal. Furthermore, in the discrete setting,by leveraging on the mathematical properties of the matrices involved, we showthat its calculations can be accelerated drastically. Finally, we present someartificial and real-life examples to show the powerfulness and performance ofthe proposed methods.

Stabilization and Variations to the Adaptive Local Iterative Filtering Algorithm: the Fast Resampled Iterative Filtering Method