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 study investigates the processing of sonar signals with neural networks for robust recognition of indoor robot environment composed of simple objects (plane, corner, edge and cylinder). The neural networks can differentiate more targets with higher accuracy. It achieves this by exploiting the identifying features extracted from sonar signals. In this paper we compare two different architectures of neural networks (global and specialized structure) in term of classification rates, the best classifier obtained is used to recognize a robot environment. The results strengthen our claims that sonar can be used as a viable system for object recognition in robotics and other application domains.

Neural Networks for Improved Target Differentiation with Sonar