智能型目标识别系统

介绍了图像目标识别技术中的图像分割，不变性参数提取和目标分类，利用图像目标的均匀性和相应知识自适应地分割和提取图像目标，被提取的每个图像目标的不变性参数由归一化过程和Zernike矩提取，并利用MPNN模型将图像目标分类，实验结果该识别系统能识别光照不均匀或复杂背景下的图像目标。The technology proposed in the image target recognition consists of image segmentation,invariant feature extraction and target classification. In this paper,a knowledge-based approach is proposed for adaptive image segmentation. Invariant features about image targets are extracted by Zernike moments.object classification is completed by the MPNN model.The experimental results show that the proposed system can be used to recognize objects from the complicated background

Expert consensus on multimodal MRI sequence parameters and scanning protocols for studies of brain and cognitive development in school‑aged children

当代多模态MRI技术的迅速发展，为精准刻画儿童青少年脑发育规律与个体差异、揭示 脑发育异常疾病的神经机制，提供了重要影像研究工具，带来了儿童脑发育影像学这一脑科学前沿方 向。然而，面向学龄儿童（6~18 岁）的高质量多模态 MRI 脑影像序列参数设置与扫描采集极具挑战 性，严重阻碍了国内脑发育相关影像研究的开展与推广。为此，国内相关领域的多家权威机构和专家 团队，依托科技部科技创新2030&ldquo; 脑科学与类脑研究&rdquo;重大项目&ldquo;中国学龄儿童脑智队列&rdquo;计划，通过 调研全国 30余家机构 MRI设备现况，以及国际大型儿童脑发育队列研究序列参数的优缺点，共同制 订了《面向学龄儿童脑智发育研究的多模态MRI脑影像序列参数与扫描规范专家共识》，以期规范国 内不同规模、不同类型队列相关脑发育影像研究中的序列参数设计与扫描操作，提升儿童脑影像扫描成功率与影像质量，以及结果可比性与稳定性，推动我国儿童脑智发育基础与临床研究的整体进步。</p

Determination of the number of ψ(3686) events taken at BESIII

The number of ψ(3686) events collected by the BESIII detector during the 2021 run period is determined to be (2259.3±11.1)×106 by counting inclusive ψ(3686) hadronic events. The uncertainty is systematic and the statistical uncertainty is negligible. Meanwhile, the numbers of ψ(3686) events collected during the 2009 and 2012 run periods are updated to be (107.7±0.6)×106 and (345.4±2.6)×106, respectively. Both numbers are consistent with the previous measurements within one standard deviation. The total number of ψ(3686) events in the three data samples is (2712.4±14.3)×10^