Abstract: Downsampling is a crucial task for processing large scale and/or dense point clouds with limited resources. Owing to the development of deep learning, approaches of task-oriented point cloud ...

Abstract: The self-attention (SA) network revisits the essence of data and has achieved remarkable results in text processing and image analysis. SA is conceptualized as a set operator that is ...

Abstract: With the rapid advancement of three-dimensional (3D) sensing technology, point cloud has emerged as one of the most important approaches for representing 3D data. However, quality ...

Abstract: Targeted fine-tuning of lightweight convolutional neural networks (CNNs) is a core approach to enabling edge devices to adapt to personalized scenarios with limited resources. However, most ...

Abstract: Point cloud upsampling (PCU) aims to transform sparse and unevenly distributed point clouds into dense and uniform counterparts with intricate geometric details of real-world objects.

The advent of the sixth-generation (6G) networks presents another round of revolution for the mobile communication landscape, promising an immersive experience, robust reliability, minimal latency, ...

The conservative organization first announced its plans for a halftime show alternative following right-wing backlash to Puerto Rican superstar Bad Bunny. By Ethan Millman Music Editor “We’re ...

Abstract: Statistical models of inter-point distances are pivotal for analyzing and optimizing wireless communication networks and other spatial systems, such as vehicular swarms and distributed ...

Abstract: Deep Neural Networks (DNNs) impose significant computational demands, necessitating optimizations for computational and energy efficiencies. Per-vector scaling, which applies a scaling ...

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

Abstract: Space-air-ground integrated networks (SAGINs) face unprecedented security challenges due to their inherent characteristics, such as multidimensional heterogeneity and dynamic topologies.

Abstract: Affective brain–computer interfaces (aBCIs) are an emerging technology that decodes brain signals—primarily electroencephalography (EEG)—to monitor and regulate emotional states in real time ...