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

Compressed sensing (CS) theory considers the restricted isometry property(RIP) as a sufficient condition for measurement matrix which guarantees therecovery of any sparse signal from its compressed measurements. The RIPcondition also preserves enough information for classification of sparsesymbols, even with fewer measurements. In this work, we utilize RIP bound asthe cost function for training a simple neural network in order to exploit thenear optimal measurements or equivalently near optimal features forclassification of a known set of sparse symbols. As an example, we considerdemodulation of pulse position modulation (PPM) signals. The results indicatethat the proposed method has much better performance than the randommeasurements and requires less samples than the optimum matched filterdemodulator, at the expense of some performance loss. Further, the proposedapproach does not need equalizer for multipath channels in contrast to theconventional receiver.

Demodulation of Sparse PPM Signals with Low Samples Using Trained RIP Matrix