跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文,不再错过。

覆盖计算机全学科,以 AI 为快捷入口。关注方向、作者和关键词,把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+1151新增论文
+896其中 AI 相关
+86已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描:2026-11-06 21:37 · 全站最近入库:2026-11-06 13:12各信源进度 →

统计口径与数据新鲜度

统计 2026-11-05 21:41 — 2026-11-06 21:41(北京时间) · 点击数字查看对应简报;AI 相关包含在新增论文内。

最近完成检查:2026-11-06 21:37最近发现新增版本:2026-11-06 13:12库内论文最新更新时间:2026-11-06 02:59

每分钟刷新已保存的数据。空检查不代表采集异常;新收录可能包含历史论文。AI 导读按新增版本计数。

17,100 已收录论文14,661 AI 相关4,055 有修订112 HF 社区发现

浏览论文库

时间范围跟随排序:版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常,查看状态 →

找到 2 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-06 21:31(北京时间)
新论文 · v1arXiv:2611.05165

QPP-Aware MCX over Tactical Edge 3D Networks in Public-Safety Communications

José Oliveira, Pedro Valente, Tiago Mendes, André Vasconcelos, Tiago Fernandes, Rhyan Matos, Duarte Raposo, Pedro Rito 等 10 位作者

During disasters, public mobile networks may become unavailable, congested, or degraded, requiring deployable communication systems to support emergency operations. This article proposes a mission-critical communications system supporing Mission Critical Push-to-Talk (MCPTT), Mission Critical Video (MCVideo), and Mission Critical Data (MCData) over a private 5G standalone network with Non-Terrestrial Network (NTN) connectivity. A context-aware Quality, Priority, and Pre-emption (QPP) mechanism is proposed to dynamically adapt network resource allocation according to the operational state of Mission Critical Services (MCX). Extensions to the Open5GS Policy Control Function (PCF) and Session Management Function (SMF) enable priority and pre-emption updates for existing flows without session re-establishment. The architecture supports tactical edge deployment of service and media functions while retaining centralized service control. Experimental evaluation considers three configurations: terrestrial backhaul with centralized media processing, satellite backhaul with centralized media processing, and satellite backhaul with edge media processing. Results show that tactical edge processing reduces communication delay and packet loss. Under satellite backhaul, edge processing reduced the 95th-percentile MCPTT audio transport delay by 60.03 ms and packet loss by 9.42 percentage points, while achieving a 95th-percentile MCPTT access time of 184.52 ms. These findings demonstrate the benefits of tactical edge media processing and provide an experimental foundation for context-aware priority mechanisms in public-safety networks integrating terrestrial and non-terrestrial components.

尚无 AI 解读,展示原文摘要

cs.NI
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02147

Refining Roadside 4D Radar Vehicle Classification Using Point-Cloud Dispersion

Mohammed Alsakabi, Andreia Figueiredo, António Almeida, John Dolan, Miguel Luís, Pedro Rito, Ozan Tonguz, Susana Sargento

Roadside sensing infrastructure is essential for Intelligent Transportation Systems (ITS), but cameras and legacy sensors are limited in range, weather robustness, and depth accuracy. 4D radar complements these modalities by resolving range, azimuth, elevation, and radial velocity under adverse conditions, though its sparse, noisy point clouds make reliable classification difficult. We present a roadside multi-sensor platform combining a 4D radar and stereo depth camera under synchronized acquisition, used to collect a real-world dataset. Our analysis reveals a substantial class-distribution discrepancy between modalities, most notably a strong radar confusion between passenger cars and motorcycles (49.01% and\ 48.60% of the dataset). To address this, we propose a Neyman-Pearson-based binary classifier on the temporally-smoothed 2D positional standard deviation of radar point cloud clusters, reducing classification error from 52.1% to 42.7%. This lightweight and edge-deployable approach improves radar-only classification reliability and lays the foundation for future camera-radar fusion.

尚无 AI 解读,展示原文摘要

cs.CV
arXiv ↗PDF ↗