[简介] 人工智能(AI)技术在制造业应用持续拓展和深化，通过构建一体化研发模式、自主化制造模式以及高韧性供应网络，驱动制造模式创新变革，成为推动制造业迈向智能化、高端化发展的核心力量。面向新发展阶段，需以场景化探索带动价值闭环验证，夯实高质量工业数据基础，推动AI与制造全流程深度融合，完善标准、生态与治理等发展保障要素，驱动工业AI迈向系统性的规模化应用，加速制造业智能化的全面跃迁。

[简介]在6G标准化商用初期（Day 1），无蜂窝技术受到了广泛关注。系统梳理了面向6G的无蜂窝通信所涉及的关键支撑技术，包括分布式收发机架构、信道信息获取与测量机制、频谱资源融合与灵活双工设计，以及分布式资源管理策略等。在此基础上，提出了一种基于数字孪生增强的无蜂窝传输优化方法，进一步提升了系统性能。同时，展示了面向高频段的无蜂窝大规模多天线（MIMO）试验验证结果，验证了相干联合传输在实际系统中的可行性。最后，探讨了无蜂窝大规模MIMO与通感一体化融合的潜在研究方向，为未来系统设计提供了新思路。

[Introduction] Millimeter-wave (mmWave) technology has been extensively studied for indoor short-range communications. In such fixed network applications, the emerging FTTR architecture allows mmWave technology to be well cascaded with in-room optical network terminals, supporting high-speed communication at rates over tens of Gb/s. In this FTTR-mmWave system, the severe signal attenuation over distance and high penetration loss through room walls are no longer bottlenecks for practical mmWave deployment. Instead, these properties create high spatial isolation, which prevents mutual interference between data streams and ensures information security. This paper surveys the promising integration of Fiber-to-the-Room (FTTR) and millimeter-wave (mmWave) access for next-generation indoor high-speed communications, with a particular focus on the Ultra-Converged Access Network (U-CAN) architecture. It is structured in two main parts: it first traces this new FTTR-mmWave architecture from the perspective of Wi-Fi and mmWave communication evolution, and then focuses specifically on the development of key mmWave chipsets for FTTR-mmW Wi-Fi applications. This work aims to provide a comprehensive reference for researchers working toward immersive, untethered indoor wireless experiences for users.

[Introduction]Open-set object detectors, as exemplified by Grounding DINO, have attracted significant attention due to their remarkable performance on in-domain datasets like Common Objects in Context (COCO) after only few-shot fine-tuning. However, their generalization capabilities in cross-domain scenarios remain substantially inferior to their in-domain few-shot performance. Prior work on fine-tuning Grounding DINO for cross-domain few-shot object detection has primarily focused on data augmentation, leaving broader systemic optimizations unexplored. To bridge this gap, we propose a comprehensive end-to-end fine-tuning framework specifically designed to optimize Grounding DINO for cross-domain few-shot scenarios. In addition, we propose Mixture-of-Experts (MoE)-Grounding DINO, a novel architecture that integrates the MoE architecture to enhance adaptability in cross-domain settings. Our approach demonstrates a significant 15.4 Mean Average Precision (mAP) improvement over the Grounding DINO baseline on the Roboflow20-VL benchmark, establishing a new state of the art for cross-domain few-shot object detection (CD-FSOD). The source code and models will be made available upon publication.