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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. Hydrogel-based DIY Underwater Morphing Artifacts: A morphing and fabrication technique to democratize the creation of controllable morphing 3D underwater structures with low-cost, easily available hydrogel beads adhered to a substrate.

   https://doi.org/10.1145/3461778.3462136  

   Jain, Harshika ; Lu, Kexin ; Yao, Lining ( June 2021 , Designing Interactive Systems Conference 2021 (DIS '21))

   null (Ed.)

   Hydrogels are versatile morphing materials that have recently been adopted for creating shape-changing interfaces. However, most shape-changing interfaces require advanced material synthesis, specialized lab settings for fabrication, and technical knowledge is needed to simulate their morphing behavior. To replicate such structures, these factors become a barrier for makers. Therefore, to democratize the creation of hydrogel-based morphing artifacts and to extend their design space in HCI, we propose a water-triggered morphing mechanism that utilizes the distance between adjacent hydrogel beads adhered on a thin substrate to control their bending angle. This paper describes the bending angle quantification experiments for creating a simulator, the process of developing a computational tool along with its user-friendly workflow and demonstrates kirigami and branch-based artifacts built with the tool. Using our method, anyone can easily design and fabricate custom morphing structures. 

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3. Dynamic Magnetic Induction Wireless Communications for Autonomous Underwater Vehicle Assisted Underwater IoT

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

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4. Compressed Underwater Acoustic Communications for Dynamic Interaction with Underwater Vehicles

   https://doi.org/10.1145/3366486.3366488  

   Rahmati, Mehdi ; Arjula, Archana ; Pompili, Dario ( October 2019 , ACM International Conference on Underwater Networks and Systems (WUWNet))
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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