Underwater Real-Time Monitoring and Information Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 444

Special Issue Editors

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: underwater signal processing; target detection; machine learning

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Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: study on the propagation characteristics of low-frequency seismic waves in the seabed; analysis of the acoustic properties of seabed sediments; research on seabed activity detection technologies

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Guest Editor
College of Physics and Optoelectronic Engineering, Harbin Engineering University, Harbin 150001, China
Interests: underwater sensors design; underwater unmanned platform

Special Issue Information

Dear Colleagues,

Underwater real-time monitoring is fundamental for establishing multi-scale, continuous, and high-precision ocean observation systems. It holds significant strategic importance for advancing our scientific understanding of the oceans, ensuring the secure utilization of marine resources, strengthening underwater safety and security, and preserving the marine ecological environment. With the rapid evolution of ocean environmental processes and the increasing frequency of underwater activities, ocean environments exhibit enhanced dynamism, increased complexity, and interwoven multi-scale effects. Traditional fixed monitoring methods, periodic observation cruises, and offline analysis workflows cannot meet the current requirements of ocean research and engineering applications. Therefore, advancing underwater real-time monitoring methods and technologies has become essential for ocean scientific research, ecological security, and deep-sea engineering. This Special Issue focuses on high-performance underwater sensors and detection methods, autonomous platforms and distributed observation networks, multi-source data fusion and intelligent processing, underwater target detection and tracking, the in situ observation of seabed mineral resources, hydrothermal activity, and magmatic processes, underwater information transmission and communication, and underwater positioning, navigation, and time synchronization (PNT). We invite original research papers and comprehensive reviews from researchers and engineers in ocean science and related fields.

Topics of interest include, but are not limited to, the following:
– High-performance underwater sensors:
– Acoustic, optical, and magnetic detection methods;
– Underwater target detection, identification, and tracking;
– Multi-sensor data fusion and intelligent processing;
– Underwater communication;
– In situ seabed observation;
– Observation of seabed mineral resources, hydrothermal activity, and magmatic processes;
– Autonomous underwater vehicles, gliders, moorings, and mobile platforms;
– Distributed observation networks;
– Machine learning;
– PNT;
– Engineering applications and sea trials.

Dr. Liming Fan
Dr. Weina Zhao
Dr. Xiaojun Zhang
Guest Editors

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Keywords

  • underwater real-time monitoring
  • high-performance sensors
  • underwater target detection and tracking
  • in situ seabed observation
  • autonomous underwater vehicles
  • distributed observation networks
  • multi-sensor fusion
  • intelligent data processing

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Published Papers (1 paper)

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Research

13 pages, 1971 KB  
Article
Prediction of Acoustic Impedance of Submarine Sediments in the Middle Area of the South Yellow Sea Using on a Random Forest Algorithm
by Xianfeng Li, Linqing Zhang, Yiming Liang, Xinfeng Hu, Kaifeng Han, Guangming Kan, Xiangmei Meng and Yong Chen
Electronics 2026, 15(5), 995; https://doi.org/10.3390/electronics15050995 - 27 Feb 2026
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Abstract
This study investigates the prediction of the acoustic impedance of submarine sediments in the middle area of the South Yellow Sea using the Random Forest (RF) model algorithm. A predictive model for the acoustic impedance of submarine sediments was established using a Random [...] Read more.
This study investigates the prediction of the acoustic impedance of submarine sediments in the middle area of the South Yellow Sea using the Random Forest (RF) model algorithm. A predictive model for the acoustic impedance of submarine sediments was established using a Random Forest algorithm based on six characteristic factors, including density, porosity, liquid limit, moisture content, plasticity index, and median particle size. The results indicate that the highest prediction accuracy and lowest error were achieved when n_estimator was set to 27, max_depth to 8, and min_samples_leaf to 7. The model significantly outperformed traditional single-parameter regression equations. The coefficient of determination (R2) of the test set reached 0.991 after model training, the mean absolute error (MAE) was 23.14 × 103 kg/(m2·s), and the mean absolute percentage error (MAPE) was 0.90%. This paper provides an in-depth analysis of the relationship between acoustic impedance and various physical and mechanical properties, providing valuable guidance for advancing the prediction of acoustic impedance of submarine sediments. Full article
(This article belongs to the Special Issue Underwater Real-Time Monitoring and Information Technologies)
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