Transmission Data
Transmission data research focuses on optimizing the efficient and reliable delivery of information across various channels, encompassing both traditional communication systems and emerging paradigms like molecular communication. Current efforts concentrate on improving energy efficiency in wireless networks using techniques like multi-hop reconfigurable intelligent surfaces and multi-agent reinforcement learning, as well as enhancing the expressiveness of data transmission in spiking neural networks through multi-bit mechanisms. These advancements are crucial for improving the performance of applications ranging from 5G networks and underwater imaging to machine learning models processing video data, ultimately impacting the efficiency and robustness of numerous technologies.
Papers
A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts
Giovanni Ziarelli, Stefano Pagani, Nicola Parolini, Francesco Regazzoni, Marco Verani
GSORB-SLAM: Gaussian Splatting SLAM benefits from ORB features and Transmittance information
Wancai Zheng, Xinyi Yu, Jintao Rong, Linlin Ou, Yan Wei, Libo Zhou