Real Time
Real-time processing focuses on developing systems capable of analyzing and responding to data instantaneously, crucial for applications demanding immediate feedback. Current research emphasizes efficient algorithms and model architectures, such as those based on deep learning, to reduce computational latency in diverse domains including robotics, healthcare, and AI-assisted tutoring. This field's advancements are driving progress in areas like autonomous navigation, personalized healthcare monitoring, and human-computer interaction, enabling more responsive and effective systems.
Papers
CPU frequency scheduling of real-time applications on embedded devices with temporal encoding-based deep reinforcement learning
Ti Zhou, Man Lin
PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
Nicolas Hubert, Pierre Monnin, Mathieu d'Aquin, Davy Monticolo, Armelle Brun
Towards Real-Time Analysis of Broadcast Badminton Videos
Nitin Nilesh, Tushar Sharma, Anurag Ghosh, C. V. Jawahar
A Mobile Data-Driven Hierarchical Deep Reinforcement Learning Approach for Real-time Demand-Responsive Railway Rescheduling and Station Overcrowding Mitigation
Enze Liu, Zhiyuan Lin, Judith Y. T. Wang, Hong Chen
MovePose: A High-performance Human Pose Estimation Algorithm on Mobile and Edge Devices
Dongyang Yu, Haoyue Zhang, Ruisheng Zhao, Guoqi Chen, Wangpeng An, Yanhong Yang
Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modeling
Xiaozheng Zheng, Zhuo Su, Chao Wen, Zhou Xue, Xiaojie Jin
Real-Time Progressive Learning: Accumulate Knowledge from Control with Neural-Network-Based Selective Memory
Yiming Fei, Jiangang Li, Yanan Li
Fourier neural operator for real-time simulation of 3D dynamic urban microclimate
Wenhui Peng, Shaoxiang Qin, Senwen Yang, Jianchun Wang, Xue Liu, Liangzhu Leon Wang