IEEE

A Sequential and Asynchronous Federated Learning Framework for Railway Point Machine Fault Diagnosis With Imperfect Data Transmission

Abstract: Fault diagnosis of railway assets has drawn the interest of both the scholarly and engineering communities. Federated learning (FL) enables training models across distributed assets to ...
IEEE

A Tutorial on Quantum Computing and Deep Learning with Quantum Deep Neural Networks

Abstract: Quantum Computing (QC) technology and Deep Learning (DL) science have garnered significant attention for their potential to revolutionize computation. This paper introduces the basic ...
IEEE

NeuPAN: Direct Point Robot Navigation With End-to-End Model-Based Learning

Abstract: Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural ...
IEEE

Enhanced Local Feature Learning With Simple Offset Attention for Semantic Segmentation of Large-Scale Point Clouds

Abstract: The semantic segmentation network performance of large-scale outdoor point clouds is usually limited by the number of input point clouds. In the application of most methods, the point cloud ...
IEEE

GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning

Abstract: Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face ...
IEEE

Eliminating Rogue Access Point Attacks in IoT: A Deep Learning Approach With Physical-Layer Feature Purification and Device Identification

Abstract: Wi-Fi plays an essential role in various emerging Internet of Things (IoT) services and applications in smart cities and communities, such as IoT access, data transmission, and intelligent ...