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

Most exiting algorithm focused on the specific emitter identification, neglected the state identification of emitter. However, using the fingerprinting to identify and estimate the user behavior is useful in many legitimate scenes.While the emitter is moving or shaking, a certain amount of vibrations are generated, which can be investigated as the basis of state identification. However, the subtle vibrations are difficult to capture. In this paper, we propose a novel state analysis technique to identify the state of emitter based on transient signal. The transient signal from the target emitters contains import special information. In order to find the subtle difference, 1-D convolution neural networks (CNNs) is introduced to extract the latent features adaptively. Experimental results have shown that our method can work for specific state identification.

Convolutional Neural Networks for Special State Identification Based on Transient Signal