Feature enhancement guided network for yield estimation of high-density jujube.

Background: Automatic and precise jujube yield prediction is important for the management of orchards and the allocation of resources. Traditional yield prediction techniques are based on object detection, which predicts a box to achieve target statistics, but are often used in sparse target settings. Those techniques, however, are challenging to use in real-world situations with particularly dense jujubes.

New results on adaptive fixed-time control for convex-delayed neural networks.

This paper studies the adaptive fixed-time synchronization issue for convex-delayed neural networks. First, the convex delay is introduced to address the state delay of neural networks in order to reflect the impacts of multiple delay components such as input transition time and switching communication. Then, a new fixed-time control method is presented to adaptively determine multi-control gains with a unified update law.