Abstract: The penetration of distributed energy resources in electrical grids has been steadily increasing in an effort to reduce greenhouse gas emissions. Inverters, as interfaces between distributed ...

Book Abstract: In 1971 Dr. Paul C. Lauterbur pioneered spatial information encoding principles that made image formation possible by using magnetic resonance signals. Now Lauterbur, "father of the MRI ...

Abstract: Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the ...

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: The goal of Optimal Transport (OT) is to define geometric tools that are useful to compare probability distributions. Their use dates back to 1781. Recent years have witnessed a new ...

Abstract: Efficient real-time decision-making for long-term multiple unmanned aerial vehicles (multi-UAV) missions in geo-distributed environments requires an integrated approach to manage dynamic ...

Abstract: The complexity of data and limited model generalization significantly hinder prediction accuracy. A physics-informed long short-term memory model with adaptive weight assignment (PILSTM-AWA) ...

Abstract: Hyperspectral image (HSI) classification is crucial in the remote sensing (RS) community. In recent years, Transformers have been popular in this field due to their global information ...

Abstract: Long-term series forecasting (LTSF) plays a crucial role in energy efficiency analysis and optimization in industrial production processes. However, due to the complexity and nonstationarity ...

Abstract: Data sparsity is the dilemma that high-resolution imaging radar often encounters in practice. Recently, sparse imaging algorithms based on compressive sensing (CS) theory have emerged as ...

Abstract: Existing unsupervised salient object detection (USOD) methods usually rely on low-level saliency priors, such as center and background priors, to detect salient objects, resulting in ...

Abstract: Recently, salient object detection (SOD) methods have achieved impressive performance. However, salient regions predicted by existing methods usually contain unsaturated regions and shadows, ...