This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

This article investigates the problem of enhancing underwater visual observations for the
purpose of accurate underwater object detection. Most existing underwater visual …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

We introduce a new semantic communication mechanism-SemanticRL, whose key idea is to
preserve the semantic information instead of strictly securing the bit-level precision. Unlike …

Conventional image classification methods usually require a large number of training
samples for the training model. However, in practical scenarios, the amount of available …

Recently, the success of non-additive steganography has demonstrated that asymmetric
distortion can remarkably improve security performance compared with symmetric cost …

High noise and low spatial resolution are two key confounding factors that limit the
qualitative and quantitative accuracy of PET images. AI models for image denoising and …

A cognitive antenna array, which is designed by using deep reinforcement learning (DRL) is
proposed in this article to adapt to the complex electromagnetic environment. Specifically …

Abstract Image Signal Processors (ISPs) convert raw sensor signals into digital images,
which significantly influence the image quality and the performance of downstream …