Abstract: The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer ...

Abstract: Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be ...

Book Abstract: "In a world of huge, interconnected networks that can be completely blacked out by disturbances, POWER SYSTEM PROTECTION offers you an improved understanding of the requirements ...

Abstract: Global Navigation Satellite Systems (GNSS) are crucial for intelligent transportation systems (ITS), providing essential positioning capabilities globally. However, in urban canyons, the ...

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 ...

Abstract: The rapidly growing importance of machine learning (ML) applications, coupled with their ever-increasing model size and inference energy footprint, has created a strong need for specialized ...

Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=6731005 ...

Abstract: Enhancing the radio access network (RAN) architecture has been the focus of some of the latest global operators’ concentrated effort, building on principles of intelligence and openness. The ...

Abstract: In recent years, Large Language Models (LLMs) have shown great abilities in various tasks, including question answering, arithmetic problem solving, and poetry writing, among others.

Abstract: This paper proposes V2Sim, an open source Pythonbased simulation platform designed for advanced vehicle-togrid (V2G) analysis in coupled urban power and transportation networks. By ...

Abstract: Current RGB-D methods usually leverage large-scale backbones to improve accuracy but sacrifice efficiency. Meanwhile, several existing lightweight methods are difficult to achieve ...

Abstract: In the semantic segmentation of remote sensing images, methods based on convolutional neural networks (CNNs) and Transformers have been extensively studied. Nevertheless, CNN struggles to ...