Combining Object Detection with Geometry-Aware Clustering to Distinguish Overlapping Plants in UAV Imagery
研究提出一种几何感知后检测框架,结合目标检测与几何聚类方法,解决无人机图像中密集作物冠层内植物重叠问题。
研究提出一种几何感知后检测框架,结合目标检测与几何聚类方法,解决无人机图像中密集作物冠层内植物重叠问题。
本文研究了如何利用RIS实现户外到室内的网络场景下的地理围栏,提出GeoRIS控制器通过管理波束对齐来控制无线服务的可用性,无需控制室外基站即可工作。
该研究提出了一种统一框架,通过扩展CG-CWM模型处理回归分析中的异质性、污染数据和缺失值问题,使用ECM算法进行参数估计。
本文提出一种基于视觉-语言模型的隐私保护语义通信框架,通过提取和移除敏感信息,并使用物理层密钥加密文本信息,减少无线边缘网络中的隐私泄露。
This work addresses the inefficiencies of Rowan University’s manual course placement and registration process, which became increasingly unsustainable amid a 57% decade-long increase in incoming student enrollment. To overcome this challenge, the authors developed the institution’s first end-to-end automated system that integrates multi-source data—including the Banner Student Information System and academic advisor-maintained Google Sheets—to intelligently group students by major, automatically assign primary and secondary courses, validate real-time capacity and scheduling constraints, and execute bulk registrations. Leveraging the Banner API, cross-platform data integration, and custom registration scripts, the system successfully enrolled over 3,500 new students, saving more than 350 staff hours annually, substantially reducing error rates, and enhancing operational efficiency and resource allocation—thereby enabling administrative personnel to focus on strategic academic advising.
研究提出一种几何感知后检测框架,结合目标检测与几何聚类方法,解决无人机图像中密集作物冠层内植物重叠问题。
本文研究了如何利用RIS实现户外到室内的网络场景下的地理围栏,提出GeoRIS控制器通过管理波束对齐来控制无线服务的可用性,无需控制室外基站即可工作。
该研究提出了一种统一框架,通过扩展CG-CWM模型处理回归分析中的异质性、污染数据和缺失值问题,使用ECM算法进行参数估计。
本文提出一种基于视觉-语言模型的隐私保护语义通信框架,通过提取和移除敏感信息,并使用物理层密钥加密文本信息,减少无线边缘网络中的隐私泄露。
This work addresses the inefficiencies of Rowan University’s manual course placement and registration process, which became increasingly unsustainable amid a 57% decade-long increase in incoming student enrollment. To overcome this challenge, the authors developed the institution’s first end-to-end automated system that integrates multi-source data—including the Banner Student Information System and academic advisor-maintained Google Sheets—to intelligently group students by major, automatically assign primary and secondary courses, validate real-time capacity and scheduling constraints, and execute bulk registrations. Leveraging the Banner API, cross-platform data integration, and custom registration scripts, the system successfully enrolled over 3,500 new students, saving more than 350 staff hours annually, substantially reducing error rates, and enhancing operational efficiency and resource allocation—thereby enabling administrative personnel to focus on strategic academic advising.