3 citations · 8 across the 7 of their papers we have counts for
7 papers
MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility
Ahsan Baidar Bakht, Zikai Jia, Muhayy ud Din +6
The underwater environment presents unique challenges, including color distortions, reduced contrast, and blurriness, hindering accurate analysis. In this work, we introduce MuLA-G…
Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions
Muhayyuddin Ahmed, Ahsan Baidar Bakht, Taimur Hassan +6
Visual perception is an important component for autonomous navigation of unmanned surface vessels (USV), particularly for the tasks related to autonomous inspection and tracking. T…
Domain-Knowledge-Aided Airborne Ground Moving Targets Tracking
Jianduo Chai, Shaoming He, Hyo-Sang Shin
This paper investigates the problem of traffic surveillance using an unmanned aerial vehicle (UAV) and proposes a domain-knowledge-aided airborne ground moving targets tracking alg…
Information-Weighted Consensus Filter with Partial Information Exchange
Byoung-Ju Jeon, Shaoming He
In this paper, the information-weighted consensus filter (ICF) with partial information exchange is proposed to reduce the bandwidth of the signals transmitted between the sensor n…
A Learning-Based Computational Impact Time Guidance
Zichao Liu, Jiang Wang, Shaoming He +2
This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem. The proposed guidance algorithm is…
A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design
Hyo-Sang Shin, Shaoming He, Antonios Tsourdos
This paper aims to examine the potential of using the emerging deep reinforcement learning techniques in flight control. Instead of learning from scratch, we suggest to leverage do…
Sample Greedy Gossip for Distributed Network-Wide Average Computation
Hyo-Sang Shin, Shaoming He, Antonios Tsourdos
This paper investigates the problem of distributed network-wide averaging and proposes a new greedy gossip algorithm. Instead of finding the optimal path of each node in a greedy m…