作者: Ma, Jinming1; Yang, Yixiao2; Tao, Ran2; Li, Gang3; Gao, Chang4 (1Information Engineering College, Capital Normal University, Beijing; 100048, China;2School of Information and Electronics, Beijing Institute of Technology, Beijing; 100081, China;3Department of Electronic and Engineering, Tsinghua University, Beijing; 100084, China;4School of Electronic Engineering, Xidian University, Shaanxi, Xi'an; 710071, China)
出处: Signal Processing 2024
作者: Pan, Wensheng1; Gao, Timin1; Zhang, Yan1; Hu, Runze3; Zheng, Xiawu1; Zhang, Enwei2; Gao, Yuting2; Liu, Yutao4; Shen, Yunhang2; Li, Ke2; Zhang, Shengchuan1; Cao, Liujuan1; Ji, Rongrong1 (1Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen; 361005, China;2Tencent Youtu Lab, Shanghai; 200233, China;3School of Information and Electronics, Beijing Institute of Technology, Beijing; 100086, China;4School of Computer Science and Technology, Ocean University of China, Qingdao; 266100, China)
出处: arXiv 2024
作者: An, Xuming1; Wang, Dui2; Shen, Li3; Luo, Yong2; Hu, Han1; Du, Bo2; Wen, Yonggang4; Tao, Dacheng3 (1School of Information and Electronics, Beijing Institute of Technology, Beijing; 100081, China;2National Engineering Research Center for Multimedia Software, School of Computer Science, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan; 430072, China;3JD Explore Academy, Beijing; 100000, China;4School of Computer Science and Engineering, Nanyang Technological University, Singapore; 639798, Singapore)
出处: arXiv 2024
作者: Gao, Yunhao1; Li, Wei1; Wang, Junjie1; Zhang, Mengmeng1; Tao, Ran1 (1Beijing Institute of Technology, School of Information and Electronics, Beijing; 100081, China)
出处: IEEE Transactions on Image Processing 2024 P3271-3284
作者: Sun, Quande1; Shan, Tao1; Feng, Yuan1; He, Sicong1, 2; Bai, Xia1; Zhang, Hongchi1 (1Beijing Institute of Technology, School of Information and Electronics, Beijing; 100081, China;2Aeronautics Computing Technology Research Institute (ACTRI), Shanxi, Xi'an; 710068, China)
出处: IEEE Sensors Journal 2024 Vol.24 No.9 P14916-14929
作者: Xia, Fanghao1; Fei, Zesong1; Wang, Xinyi1; Liu, Peng1; Guo, Jing1; Wu, Qingqing2 (1Beijing Institute of Technology, School of Information and Electronics, Beijing; 100081, China;2Shanghai Jiao Tong University, Department of Electronic Engineering, Shanghai; 200240, China)
出处: IEEE Wireless Communications Letters 2024 Vol.13 No.5 P1523-1527
作者: Cui, Yi1, 2, 3; Xin, Xiangjun4; Gao, Ran4; Zhang, Qi1, 2, 3; Yao, Haipeng5 (1School of Electronic Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing; 100876, China;2Beijing Key Laboratory of Space-Ground Interconnection and Convergence, BUPT, Beijing; 100876, China;3State Key Laboratory of Information Photonics and Optical Communications, BUPT, Beijing; 100876, China;4School of Information and Electronics, Beijing Institute of Technology (BIT), Beijing; 100081, China;5School of Information and Communication Engineering, BUPT, Beijing; 100876, China)
出处: Optics Express 2024 Vol.32 No.8 P13640-13656
作者:
Yongli Xu1,2,3;Hanruo Liu4,5,3;Run Sun2;Huaizhou Wang4;Yanjiao Huo4;Ningli Wang4,6;
出处: Heliyon 2024 Vol.10 No.13 Pe33813
关键词: Deep learning;Retinal nerve fiber layer thickness;Fundus photography;Optical coherence tomography;Glaucoma
摘要: Purpose This study aimed to propose a new deep learning (DL) approach to automatically predict the retinal nerve fiber layer thickness (RNFLT) around ...
作者: 沈甜雨1;,李志伟1;,范丽丽2;,张庭祯1;,唐丹丹3;,周美华4;,刘华平5;,王坤峰1; (1北京化工大学信息科学与技术学院;2北京理工大学信息与电子学院;3燕山大学信息科学与工程学院;4北京同仁医院眼科研究所;5清华大学计算机科学与技术系)
出处: 智能科学与技术学报 2024 第6卷 第1期 P17-32
关键词: 具身智能;自动驾驶;具身感知;具身执行;具身进化
摘要: 具身智能突破了传统人工智能的界限,强调了机器与物理世界交互的重要性,旨在通过促进软硬件一体化智能体的环境适应性学习和智能行为的演化,解决更多智能系统在现实应用中的问题。在这一理念的启发下,提出了具身智能驾驶的概念与框架,将具身智能思想融入自动驾驶汽车的开发与应用中,通过物理智能体、虚拟智能体和真实交 ...
作者: 翁国翔1;,田清华1;,王富1;,田凤1;,张琦1;,杨雷静1;,忻向军2; (1北京邮电大学电子工程学院,信息光子学与光通信国家重点实验室,天地互联与融合北京市重点实验室;2北京理工大学信息与电子学院)
出处: 光学学报 2024 第44卷 第11期 P31-37
关键词: 光通信;平方根容积卡尔曼滤波算法;偏振复用;偏振态旋转;残差判决
摘要: 针对相干光通信系统中,传统容积卡尔曼滤波(CKF)算法和平方根容积卡尔曼滤波(SCKF)算法对偏振态旋转(RSOP)均衡存在鲁棒性不足、泛化性弱等问题,提出了一种新型的自适应SCKF算法,以实现对RSOP的跟踪补偿。该算法通过引入平方根因子直接对过程噪声协方差矩阵的平方根进行自适应更新,避免了正定分 ...