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
Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time Systems: An Empirical Investigation
Tushar Prasanna Swaminathan, Christopher Silver, Thangarajah Akilan
Real-Time Remote Control via VR over Limited Wireless Connectivity
H. P. Madushanka, Rafaela Scaciota, Sumudu Samarakoon, Mehdi Bennis
Real-time, accurate, and open source upper-limb musculoskeletal analysis using a single RGBD camera
Amedeo Ceglia, Kael Facon, Mickaël Begon, Lama Seoud
Think Deep and Fast: Learning Neural Nonlinear Opinion Dynamics from Inverse Dynamic Games for Split-Second Interactions
Haimin Hu, Jonathan DeCastro, Deepak Gopinath, Guy Rosman, Naomi Ehrich Leonard, Jaime Fernández Fisac
Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objects
Yizhou Chen, Yiting Zhang, Zachary Brei, Tiancheng Zhang, Yuzhen Chen, Julie Wu, Ram Vasudevan
FlightBench: Benchmarking Learning-based Methods for Ego-vision-based Quadrotors Navigation
Shu-Ang Yu, Chao Yu, Feng Gao, Yi Wu, Yu Wang