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/

Zendy Plus

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

Zendy Tools

Zendy Open

In recent years, there is a significant increase in the number of devices with low power consumption. The energy requirements of these devices are provided by chemical batteries. The batteries must be charged at regular times, and cause some problems such as environmental pollution. RF energy harvesters are an alternative energy source for the batteries. In this study, the responses of 900 MHz RF energy harvester, which was previously tested, are estimated using an Artificial Neural Network (ANN) method in different states. For this aim, the output power values are determined by using the input power and the frequency of the signal and the load resistances connected to the energy harvester.

THE EFFICIENCY ESTIMATION OF 900 MHZ RF ENERGY HARVESTER USING ARTIFICIAL NEURAL NETWORK