Abstract: Metal halide perovskite solar cells (PSCs) have made substantial progress in power conversion efficiency (PCE) and stability in the past decade thanks to the advancements in perovskite ...

Abstract: Maxwell's equations are replaced by a set of finite difference equations. It is shown that if one chooses the field points appropriately, the set of finite difference equations is applicable ...

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past ...

This book presents an original generalized transmission line approach associated with non-resonant structures that exhibit larger bandwidths, lower loss, and higher design flexibility. It is based on ...

Abstract: Emerging applications, such as autonomous driving and Internet of Things (IoT) services put forward the demand for simultaneous sensing and communication functions in the same system.

Abstract: The maneuvering of a large-scale unmanned aerial vehicle (UAV) swarm, notable for flexible flight with collision-free, is still challenging due to the significant number of UAVs and the ...

Abstract: Real-time image dehazmg is crucial for applications such as autonomous driving, surveillance, and remote sensing, where haze can significantly reduce visibility. However, many deep learning ...

Abstract: In addition to enhancing wireless communication coverage quality, reconfigurable intelligent surface (RIS) technique can also assist in positioning. In this work, we consider RIS-assisted ...

Abstract: Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data. However, ...

Abstract: Object detection methods using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with adverse weather due to a ...

Abstract: Recent studies have integrated convolutions into transformers to introduce inductive bias and improve generalization performance. However, the static nature of conventional convolution ...

Abstract: Load forecasting and renewable energy forecasting are fundamental for the optimization and scheduling of new power systems, which play a crucial role in ensuring the safe, stable, and ...