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

This work treats the recovery of sparse, binary signals throughbox-constrained basis pursuit using biased measurement matrices. Using aprobabilistic model, we provide conditions under which the recovery of bothsparse and saturated binary signals is very likely. In fact, we also show thatunder the same condition, the solution of the boxed-constrained basis pursuitprogram can be found using boxed-constrained least-squares.

Recovery of Binary Sparse Signals with Biased Measurement Matrices