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 paper describes computationally efficient approaches and associatedtheoretical performance guarantees for the detection of known targets andanomalies from few projection measurements of the underlying signals. Theproposed approaches accommodate signals of different strengths contaminated bya colored Gaussian background, and perform detection without reconstructing theunderlying signals from the observations. The theoretical performance bounds ofthe target detector highlight fundamental tradeoffs among the number ofmeasurements collected, amount of background signal present, signal-to-noiseratio, and similarity among potential targets coming from a known dictionary.The anomaly detector is designed to control the number of false discoveries.The proposed approach does not depend on a known sparse representation oftargets; rather, the theoretical performance bounds exploit the structure of aknown dictionary of targets and the distance preservation property of themeasurement matrix. Simulation experiments illustrate the practicality andeffectiveness of the proposed approaches.

Target Detection Performance Bounds in Compressive Imaging