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

Anomalies in the ambient magnetic field can be used as features in indoorpositioning and navigation. By using Maxwell's equations, we derive and presenta Bayesian non-parametric probabilistic modeling approach for interpolation andextrapolation of the magnetic field. We model the magnetic field componentsjointly by imposing a Gaussian process (GP) prior on the latent scalarpotential of the magnetic field. By rewriting the GP model in terms of aHilbert space representation, we circumvent the computational pitfallsassociated with GP modeling and provide a computationally efficient andphysically justified modeling tool for the ambient magnetic field. The modelallows for sequential updating of the estimate and time-dependent changes inthe magnetic field. The model is shown to work well in practice in differentapplications: we demonstrate mapping of the magnetic field both with aninexpensive Raspberry Pi powered robot and on foot using a standard smartphone.

Modeling and interpolation of the ambient magnetic field by Gaussian processes