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   https://doi.org/10.1109/ICC45855.2022.9839033  

   Unal, Deniz; Falleni, Sara; Demirors, Emrecan; Enhos, Kerem; Basagni, Stefano; Melodia, Tommaso (May 2022, IEEE International Conference on Communications)
2. Soft Underwater Robots

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3. Mapping of Underwater Structures by a Team of Autonomous Underwater Vehicles

   Xanthidis, M.; Joshi, B.; Roznere, M.; Wang, W.; Burgdorfer, N.; Quattrini Li, A.; Mordohai, P.; Nelakuditi, S.; Rekleitis, I. (January 2022, International Symposium of Robotics Research)
4. Dynamic Magnetic Induction Wireless Communications for Autonomous Underwater Vehicle Assisted Underwater IoT

   https://doi.org/10.1109/JIOT.2020.2997709  

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5. Comprehensive Underwater Object Tracking Benchmark Dataset and Underwater Image Enhancement With GAN

   https://doi.org/10.1109/JOE.2021.3086907  

   Panetta, Karen; Kezebou, Landry; Oludare, Victor; Agaian, Sos (January 2021, IEEE Journal of Oceanic Engineering)

   Abstract—Current state-of-the-art object tracking methods have largely benefited from the public availability of numerous benchmark datasets. However, the focus has been on open-air imagery and much less on underwater visual data. Inherent underwater distortions, such as color loss, poor contrast, and underexposure, caused by attenuation of light, refraction, and scattering, greatly affect the visual quality of underwater data, and as such, existing open-air trackers perform less efficiently on such data. To help bridge this gap, this article proposes a first comprehensive underwater object tracking (UOT100) benchmark dataset to facilitate the development of tracking algorithms well-suited for underwater environments. The proposed dataset consists of 104 underwater video sequences and more than 74 000 annotated frames derived from both natural and artificial underwater videos, with great varieties of distortions. We benchmark the performance of 20 state-of-the-art object tracking algorithms and further introduce a cascaded residual network for underwater image enhancement model to improve tracking accuracy and success rate of trackers. Our experimental results demonstrate the shortcomings of existing tracking algorithms on underwater data and how our generative adversarial network (GAN)-based enhancement model can be used to improve tracking performance. We also evaluate the visual quality of our model’s output against existing GAN-based methods using well-accepted quality metrics and demonstrate that our model yields better visual data. Index Terms—Underwater benchmark dataset, underwater generative adversarial network (GAN), underwater image enhancement (UIE), underwater object tracking (UOT). 

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