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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This work concerns noise reduction for one-dimensional spectra in the casethat the signal is corrupted by an additive white noise. The proposed methodstarts with mapping the noisy spectrum to a partial circulant matrix. In virtueof singular-value decomposition of the matrix, components belonging to thesignal are determined by inspecting the total variations of left singularvectors. Afterwards, a smoothed spectrum is reconstructed from the low-rankapproximation of the matrix consisting of the signal components only. Thedenoising effect of the proposed method is shown to be highly competitive amongother existing nonparametric methods, including moving average, waveletshrinkage, and total variation. Furthermore, its applicable scenarios inprecision storage-ring mass spectrometry are demonstrated to be rather diverseand appealing.

Denoising scheme based on singular-value decomposition for one-dimensional spectra and its application in precision storage-ring mass spectrometry