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

There have been many works that focus on the sampling set design for a staticgraph signal, but few for time-varying graph signals (GS). In this paper, weconcentrate on how to select vertices to sample and how to allocate thesampling budget for a time-varying GS to achieve a minimal tracking error forthe long-term. In the Kalman Filter (KF) framework, the problem of samplingpolicy design and budget allocation is formulated as an infinite horizonsequential decision process, in which the optimal sampling policy is obtainedby Dynamic Programming (DP). Since the optimal policy is intractable, anapproximate algorithm is proposed by truncating the infinite horizon. Byintroducing a new tool for analyzing the convexity or concavity of compositefunctions, we prove that the truncated problem is convex. Finally, wedemonstrate the performance of the proposed approach through numericalexperiments.

Sampling Policy Design for Tracking Time-Varying Graph Signals with Adaptive Budget Allocation