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

Polynomial graph filters and their inverses play important roles in graphsignal processing. An advantage of polynomial graph filters is that they can beimplemented in a distributed manner, which involves data transmission betweenadjacent vertices only. The challenge arisen in the inverse filtering is that adirect implementation may suffer from high computational burden, as the inversegraph filter usually has full bandwidth even if the original filter has smallbandwidth. In this paper, we consider distributed implementation of the inversefiltering procedure for a polynomial graph filter of multiple shifts, and wepropose two iterative approximation algorithms that can be implemented in adistributed network, where each vertex is equipped with systems for limiteddata storage, computation power and data exchanging facility to its adjacentvertices. We also demonstrate the effectiveness of the proposed iterativeapproximation algorithms to implement the inverse filtering procedure and theirsatisfactory performance to denoise time-varying graph signals and a data setof US hourly temperature at 218 locations.

Polynomial graph filter of multiple shifts and distributed implementation of inverse filtering