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

We present a novel method for inferring ground-truth signal from multipledegraded signals, affected by different amounts of sensor exposure. Thealgorithm learns a multiplicative degradation effect by performing iterativecorrections of two signals solely from the ratio between them. The degradationfunction d should be continuous, satisfy monotonicity, and d(0) = 1. We usesmoothed monotonic regression method, where we easily incorporate theaforementioned criteria to the fitting part. We include theoretical analysisand prove convergence to the ground-truth signal for the noiseless measurementmodel. Lastly, we present an approach to fuse the noisy corrected signals usingGaussian processes. We use sparse Gaussian processes that can be utilized for alarge number of measurements together with a specialized kernel that enablesthe estimation of noise values of all sensors. The data fusion frameworknaturally handles data gaps and provides a simple and powerful method forobserving the signal trends on multiple timescales(long-term and short-termsignal properties). The viability of correction method is evaluated on asynthetic dataset with known ground-truth signal.

Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method