Tianshun Han
Papers
1
Total Citations
3
H-Index
1
About
Tianshun Han is a researcher whose work lies at the intersection of underwater acoustics and deep learning, with a particular focus on improving communication reliability in challenging marine environments. His most cited paper, "A Convolutional Neural Network Based BPSK Demodulator for Underwater Acoustic Communication" (2022), introduces a novel approach to signal demodulation that leverages convolutional neural networks to overcome the persistent challenges of propagation delay, multipath interference, and Doppler effects inherent to underwater channels. This work, which has garnered 3 citations, represents an early but promising contribution to the growing field of intelligent underwater sensor networks. By replacing traditional demodulation techniques with a data-driven neural architecture, Han demonstrates how machine learning can enhance the robustness and efficiency of underwater acoustic communication systems. His research is particularly relevant for applications in ocean monitoring, autonomous underwater vehicles, and marine data collection, where reliable data transmission is critical. As the demand for underwater connectivity grows, Han's work provides a foundation for more adaptive and resilient communication protocols in one of the most difficult transmission environments on Earth.
Research Focus
Key Achievements
Top Papers
- 1