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1. Visual Tracking of Deepwater Animals Using Machine Learning-Controlled Robotic Underwater Vehicles

   Katija, K; Roberts, PLD; Daniels, J; Lapides, A; Barnard, K; Risi, M; Ranaan, BY; Woodward, BG; Takahashi, J (January 2021, IEEE Winter Conference on Applications of Computer Vision)

   null (Ed.)

   The ocean is a vast three-dimensional space that is poorly explored and understood, and harbors unobserved life and processes that are vital to ecosystem function. To fully interrogate the space, novel algorithms and robotic platforms are required to scale up observations. Locating animals of interest and extended visual observations in the water column are particularly challenging objectives. Towards that end, we present a novel Machine Learning-integrated Tracking (or ML-Tracking) algorithm for underwater vehicle control that builds on the class of algorithms known as tracking-by-detection. By coupling a multi-object detector (trained on in situ underwater image data), a 3D stereo tracker, and a supervisor module to oversee the mission, we show how ML-Tracking can create robust tracks needed for long duration observations, as well as enable fully automated acquisition of objects for targeted sampling. Using a remotely operated vehicle as a proxy for an autonomous underwater vehicle, we demonstrate continuous input from the ML-Tracking algorithm to the vehicle controller during a record, 5+ hr continuous observation of a midwater gelatinous animal known as a siphonophore. These efforts clearly demonstrate the potential that tracking-by-detection algorithms can have on exploration in unexplored environments and discovery of undiscovered life in our ocean. 

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2. 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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3. Hierarchical Classic Controllers (HCC) with an Enhanced SVM Method for DDoS Attack Detection in SDN

   https://doi.org/10.1109/ANTS56424.2022.10227717  

   Mehr, Shideh Yavary; Ramamurthy, Byrav (December 2022, 2022 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS))

   A centralized Software-defined Network (SDN) controller, due to its nature, faces many issues such as a single point of failure, computational complexity growth, different types of attacks, reliability challenges and scalability concerns. One of the most common fifth generation cyber-attacks is the Distributed Denial of Service (DDoS) attack. Having a single SDN controller can lead to a plethora of issues with respect to latency, computational complexity in the control plane, reachability, and scalability as the network scale increases. To address these issues, state-of-the-art approaches have investigated multiple SDN controllers in the network. The placement of these multiple controllers has drawn more attention in recent studies. In our previous work, we evaluated an Entropy-based technique and a machine learning-based Support Vector Machine (SVM) to detect DDoS using a single SDN controller. In this paper, we extend our previous work to further decrease the impact of the DDoS attacks on the SDN controller. Our new technique called Hierarchical Classic Controllers (HCC) uses SVM and Entropy methods to detect abnormal traffic which can lead to network failures caused by overwhelming a single controller. Determining the number of controllers and their best placement are major contributions in our new method. Our results show that the combination of the above three methods (HCC with SVM and Entropy), in the case of a network with 3 controllers provides greater accuracy and improves the DDoS attack detection rate to 86.12% compared to 79.03% and 81.33% using Entropy-based HCC and SVM-based HCC, respectively. 

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4. Advancing flame retardant prediction: A self-enforcing machine learning approach for small datasets

   https://doi.org/10.1063/5.0152195  

   Yan, Cheng; Lin, Xiang; Feng, Xiaming; Yang, Hongyu; Mensah, Patrick; Li, Guoqiang (June 2023, Applied Physics Letters)

   Improving the fireproof performance of polymers is crucial for ensuring human safety and enabling future space colonization. However, the complexity of the mechanisms for flame retardant and the need for customized material design pose significant challenges. To address these issues, we propose a machine learning (ML) framework based on substructure fingerprinting and self-enforcing deep neural networks (SDNN) to predict the fireproof performance of flame-retardant epoxy resins. Our model is based on a comprehensive understanding of the physical mechanisms of materials and can predict fireproof performance and eliminate the needs for properties descriptors, making it more convenient than previous ML models. With a dataset of only 163 samples, our SDNN models show an average prediction error of 3% for the limited oxygen index (LOI). They also provide satisfactory predictions for the peak of heat release rate PHR and total heat release (THR), with coefficient of determination (R2) values of 0.87 and 0.85, respectively, and average prediction errors less than 17%. Our model outperforms the support vector model SVM for all three indices, making it a state-of-the-art study in the field of flame retardancy. We believe that our framework will be a valuable tool for the design and virtual screening of flame retardants and will contribute to the development of safer and more efficient polymer materials. 

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5. Towards usable underwater virtual reality systems

   https://doi.org/10.1109/VR.2017.7892281  

   Costa, Raphael; Guo, Rongkai; Quarles, John (March 2017, Virtual Reality (VR), 2017 IEEE)

   The objective of this research is to compare the effectiveness of different tracking devices underwater. There have been few works in aquatic virtual reality (VR) - i.e., VR systems that can be used in a real underwater environment. Moreover, the works that have been done have noted limitations on tracking accuracy. Our initial test results suggest that inertial measurement units work well underwater for orientation tracking but a different approach is needed for position tracking. Towards this goal, we have waterproofed and evaluated several consumer tracking systems intended for gaming to determine the most effective approaches. First, we informally tested infrared systems and fiducial marker based systems, which demonstrated significant limitations of optical approaches. Next, we quantitatively compared inertial measurement units (IMU) and a magnetic tracking system both above water (as a baseline) and underwater. By comparing the devices rotation data, we have discovered that the magnetic tracking system implemented by the Razer Hydra is more accurate underwater as compared to a phone-based IMU. This suggests that magnetic tracking systems should be further explored for underwater VR applications. 

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