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

This paper introduces a spatio-temporal resonator model and an inferencemethod for detection and estimation of nearly periodic temporal phenomena inspatio-temporal data. The model is derived as a spatial extension of astochastic harmonic resonator model, which can be formulated in terms of astochastic differential equation (SDE). The spatial structure is included byintroducing linear operators, which affect both the oscillations and damping,and by choosing the appropriate spatial covariance structure of the drivingtime-white noise process. With the choice of the linear operators as partialdifferential operators, the resonator model becomes a stochastic partialdifferential equation (SPDE), which is compatible with infinite-dimensionalKalman filtering. The resulting infinite-dimensional Kalman filtering problemallows for a computationally efficient solution as the computational costscales linearly with measurements in the temporal dimension. This framework isapplied to weather prediction and to physiological noise elimination in fMRIbrain data.

Infinite-dimensional Bayesian filtering for detection of quasi-periodic phenomena in spatio-temporal data