Search Results (44)

With the rapid advancement of deep learning techniques, side-scan sonar image segmentation has become a crucial task in underwater scene understanding. However, the complex and variable underwater environment poses significant challenges for salient object detection, with traditional deep learning approaches often suffering from inadequate feature representation and the loss of global context during downsampling, thus compromising the segmentation accuracy of fine structures. To address these issues, we propose SonarNet, a Global Feature-Based Hybrid Attention Network specifically designed for side-scan sonar image segmentation. SonarNet features a dual-encoder architecture that leverages residual blocks and a self-attention mechanism to simultaneously capture both global structural and local contextual information. In addition, an adaptive hybrid attention module is introduced to effectively integrate channel and spatial features, while a global enhancement block fuses multi-scale global and spatial representations from the dual encoders, mitigating information loss throughout the network. Comprehensive experiments on a dedicated underwater sonar dataset demonstrate that SonarNet outperforms ten state-of-the-art saliency detection methods, achieving a mean absolute error as low as 2.35%. These results highlight the superior performance of SonarNet in challenging sonar image segmentation tasks. Full article

The marine environment is complex and variable, with the absorption and scattering effects of seawater and turbulence causing significant attenuation of received optical signals and introducing random jitter, which limits the communication range and stability of underwater wireless optical communication systems. This paper presents the Superposition UEP-Spinal Code structure, which utilizes unequal error protection (UEP) to adjust the transmission performance of different types of information in underwater composite data communication by adjusting the superposition weighting factors in the encoding algorithm. This encoding method enhances the noise immunity of important data, and with the same bandwidth utilization, the overall decoding complexity is reduced by 13.3% compared to the previously improved Spinal code encoding algorithm. The results show that the Superposition UEP-Spinal Code provides a more stable, reliable, and efficient communication solution for underwater wireless optical communication systems with randomly varying channel conditions. Full article

We investigate the underwater propagation of multi-Gaussian correlated asymmetric Bessel beam with partial coherence in the condition of quadrature amplitude modulation. The oceanic turbulence optical power spectrum is used to characterize turbulence effects under variable temperature and salinity. Based on the derivation of orbital angular momentum mode distribution, the theoretical model of bit error rate (BER) is constructed. Numerical analyses show that the low-temperature oceanic channel is more beneficial to BER reduction than the low-salinity channel. Due to the better resistance to turbulence, low-order modulation is superior in BER performance. As for beam optimization, the increments in wavelength and source coherence width, or the decrements of topological charge and asymmetry factor, help to obtain a lower BER. The research is instructive for the construction of underwater transmission links based on vortex beams. Full article

Variable marine environmental conditions, particularly at the sea surface, present considerable challenges to cross-media laser transmission. This study simulates uplink laser transmission through a seawater–sea surface–air channel via ray tracing and Monte Carlo methods, with an emphasis on the impacts of the sea surface channel. A spatial model of the sea surface is introduced, which uses a wave spectrum and fast Fourier transform technology, and the results are compared against those of a classical statistical model. The validity and applicability of six representative wind wave spectra are assessed for their effectiveness in characterizing the optical sea surface. Among these spectra, the Elfouhaily spectrum, which is refined for low-wind conditions, can most accurately represent the optical properties of the sea surface. The simulations reveal that the spatial model captures power fluctuations due to dynamic sea surface changes. At shorter underwater transmission distances, the spatial model may induce considerable drift, thereby degrading power estimates, where the difference is about 0.9 dB compared with the statistical model. Deeper underwater transmissions can mitigate beam distortions, resulting in a decrease in normalized peak power from −114 dB to −157 dB. Additionally, the laser centroid distribution tends to be elliptical because of the distribution of the sea surface azimuth. These findings underscore the importance of incorporating spatiotemporal dynamics in modeling sea surfaces and provide insights for optimizing underwater air laser transmission links in complex marine environments. Full article

This work presents a novel approach to modeling an underwater optical wireless communications (UOWC) channel based on a deterministic analysis specifically for non-line-of-sight (NLOS) configurations. The model considers the presence of a wavy ocean surface, providing a more accurate representation of realistic conditions. By expanding the possibilities for communication in complex underwater environments, our model offers a comprehensive analysis of the ocean waves’ impact. A significant achievement of this study is the capacity of the model to accurately compute the variable size of the width of the beam (footprint) on the receiver plane reflected by the sea surface and the time intervals during which the receiver remains illuminated. Additionally, the model determines the precise position of the reflected beam on the receiver plane and accurately identifies the time intervals during which communication is feasible, offering invaluable insight into the system performance under oceanic wave variability. The results confirmed that oceanic wave variability induces severe misalignment in optical links, creating intermittent opportunities for effective communication. The optical–geometric analysis contributed significantly to understanding the novel impact of ocean waves on NLOS-UOWC systems. These findings enhance the preliminary considerations in NLOS link design, particularly in scenarios with autonomous underwater vehicles in constant motion, aiding in the reduction of pointing errors. Full article

Underwater acoustic (UWA) communication encounters significant challenges, including impulsive noise from breaking waves and marine organisms, as well as long-delay taps caused by ocean properties and high transmission rates. To address these issues, we enhance the channel estimation process by introducing iteratively reweighted least squares (IRLS) methods and propose an impulsive noise suppression algorithm. Furthermore, we analyze the inter-frequency interference (IFI) resulting from channel variability and implement IFI cancellation (IFIC) during iterative processing. Furthermore, an IFIC-based dual decision–feedback equalization (DDFE) algorithm is proposed for fast time-varying channels, enabling a considerable reduction in channel length and subsequent equalizer complexity. The proposed IFIC-based DDFE algorithm with impulsive noise suppression has been validated through sea trial data, demonstrating robustness against impulsive noise. Experimental results indicate that the proposed algorithm reduces click signal energy and significantly improves receiver performance compared to traditional DDFE algorithms. This research highlights the effectiveness of adapted UWA communication strategies in environments characterized by impulsive noise and long delay taps, facilitating more reliable UWA communication. Full article

Accurate river hydraulic characterization is fundamental to assess flood risk, parametrize flood forecasting models, and develop river maintenance workflows. River hydraulic roughness and riverbed/floodplain geometry are the main factors controlling inundation extent and water levels. However, gauging stations providing hydrometric observations are declining worldwide, and they provide point measurements only. To describe hydraulic processes, spatially distributed data are required. In situ surveys are costly and time-consuming, and they are sometimes limited by local accessibility conditions. Satellite earth observation (EO) techniques can be used to measure spatially distributed hydrometric variables, reducing the time and cost of traditional surveys. Satellite EO provides high temporal and spatial frequency, but it can only measure large rivers (wider than ca. 50 m) and only provides water surface elevation (WSE), water surface slope (WSS), and surface water width data. UAS hydrometry can provide WSE, WSS, water surface velocity and riverbed geometry at a high spatial resolution, making it suitable for rivers of all sizes. The use of UAS hydrometry can enhance river management, with cost-effective surveys offering large coverage and high-resolution data, which are fundamental in flood risk assessment, especially in areas that difficult to access. In this study, we proposed a combination of UAS hydrometry techniques to fully characterize the hydraulic parameters of a river. The land elevation adjacent to the river channel was measured with LiDAR, the riverbed elevation was measured with a sonar payload, and the WSE was measured with a UAS radar altimetry payload. The survey provided 57 river cross-sections with riverbed elevation, and 8 km of WSE and land elevation and took around 2 days of survey work in the field. Simulated WSE values were compared to radar altimetry observations to fit hydraulic roughness, which cannot be directly observed. The riverbed elevation cross-sections have an average error of 32 cm relative to RTK GNSS ground-truth measurements. This error was a consequence of the dense vegetation on land that prevents the LiDAR signal from reaching the ground and underwater vegetation, which has an impact on the quality of the sonar measurements and could be mitigated by performing surveys during winter, when submerged vegetation is less prevalent. Despite the error of the riverbed elevation cross-sections, the hydraulic model gave good estimates of the WSE, with an RMSE below 3 cm. The estimated roughness is also in good agreement with the values measured at a gauging station, with a Gauckler–Manning–Strickler coefficient of M = 16–17 m^1/3/s. Hydraulic modeling results demonstrate that both bathymetry and roughness measurements are necessary to obtain a unique and robust hydraulic characterization of the river. Full article

To minimize the temperature of the propulsion motor and reduce flow loss in the water-cooling structure during the operation of an underwater unmanned vehicle, this paper employs a multi-objective genetic algorithm to optimize the dimensions of the inner and outer dual-channel water-cooling structure as well as the flow rate of the cooling water. Firstly, the influence of design variables on response variables was examined through sensitivity analysis. Subsequently, a model sample library for simulating the coupled temperature and flow fields of the motor was constructed, and a response surface model between the variables was developed. Finally, appropriate sample points were selected from the Pareto solution set to verify the validity of the optimization results through CFD simulation and error analysis. The sensitivity analysis results indicate that the cooling water flow rate had the greatest impact on both the maximum motor temperature and the flow losses of the water-cooling structure, with values of 77.79% and 99.84%, respectively. On the other hand, the optimal design parameters for the four dimensions of the channel and the cooling water flow rate were obtained. Compared with the initial dimensions of the water-cooling structure, the maximum temperature of the motor decreased from 332.86 K to 331.46 K. Simultaneously, the flow loss of the water-cooling structure decreased from 100.02 kPa to 59.58 kPa, with a maximum reduction rate of 40.43%. The optimization effect of the motor cooling system is significant, which provides valuable insights for system design under the premise of ignoring multi-objective interactions. Full article

The Black Sea, a unique semi-enclosed marine ecosystem, is the eastern maritime boundary of the European Union and holds significant ecological importance. The present study investigates anthropogenic noise pollution in the context of the Marine Strategy Framework Directive’s Descriptor 11, with a particular emphasis on the criteria for impulsive sound (D11C1) and continuous low-frequency sound (D11C2) in Romanian ports, which handle a substantial share of regional cargo traffic, and impact maritime activities and associated noise levels. The noise levels from shipping activity vary across Romanian waters, including territorial waters, the contiguous zone, and the Exclusive Economic Zone. These areas are classified by high, medium, and low ship traffic density. Ambient noise levels at frequencies of 63 Hz and 125 Hz, dominated by shipping noise, were established, along with their hydrospatial distribution for the 2019–2020 period. Furthermore, predictive modeling techniques are used in this study to assess underwater noise pollution from human sources. This modeling effort represents the first initiative in the region and utilizes the BELLHOP ray-tracing method for underwater acoustic channel modeling in shallow-water environments. The model incorporates realistic bathymetry, oceanography, and geology features for environmental input, allowing for improved prediction of acoustic variability due to time-varying sea variations in shallow waters. The study’s findings have important implications for understanding and mitigating anthropogenic noise pollution’s impact on the Black Sea marine ecosystem. Full article

Underwater object detection and classification pose significant challenges due to environmental factors such as water turbidity and variable lighting conditions. This research proposes a novel approach that integrates advanced imaging techniques with diffusion models to address these challenges effectively, aligning with Sustainable Development Goal (SDG) 14: Life Below Water. The methodology leverages the Convolutional Block Attention Module (CBAM), Modified Swin Transformer Block (MSTB), and Diffusion model to enhance the quality of underwater images, thereby improving the accuracy of object detection and classification tasks. This study utilizes the TrashCan dataset, comprising diverse underwater scenes and objects, to validate the proposed method’s efficacy. This study proposes an advanced imaging technique YOLO (you only look once) network (AIT-YOLOv7) for detecting objects in underwater images. This network uses a modified U-Net, which focuses on informative features using a convolutional block channel and spatial attentions for color correction and a modified swin transformer block for resolution enhancement. A novel diffusion model proposed using modified U-Net with ResNet understands the intricate structures in images with underwater objects, which enhances detection capabilities under challenging visual conditions. Thus, AIT-YOLOv7 net precisely detects and classifies different classes of objects present in this dataset. These improvements are crucial for applications in marine ecology research, underwater archeology, and environmental monitoring, where precise identification of marine debris, biological organisms, and submerged artifacts is essential. The proposed framework advances underwater imaging technology and supports the sustainable management of marine resources and conservation efforts. The experimental results demonstrate that state-of-the-art object detection methods, namely SSD, YOLOv3, YOLOv4, and YOLOTrashCan, achieve mean accuracies (mAP@0.5) of 57.19%, 58.12%, 59.78%, and 65.01%, respectively, whereas the proposed AIT-YOLOv7 net reaches a mean accuracy (mAP@0.5) of 81.4% on the TrashCan dataset, showing a 16.39% improvement. Due to this improvement in the accuracy and efficiency of underwater object detection, this research contributes to broader marine science and technology efforts, promoting the better understanding and management of aquatic ecosystems and helping to prevent and reduce the marine pollution, as emphasized in SDG 14. Full article

The wavelength attack utilizes the dependence of beam splitters (BSs) on wavelength to cause legitimate users Alice and Bob to underestimate their excess noise so that Eve can steal more secret keys without being detected. Recently, the wavelength attack on Gaussian-modulated continuous-variable quantum key distribution (CV-QKD) has been researched in both fiber and atmospheric channels. However, the wavelength attack may also pose a threat to the case of ocean turbulent channels, which are vital for the secure communication of both ocean sensor networks and submarines. In this work, we propose two wavelength attack schemes on underwater discrete modulated (DM) CV-QKD protocol, which is effective for the case with and without local oscillator (LO) intensity monitor, respectively. In terms of the transmittance properties of the fused biconical taper (FBT) BS, two sets of wavelengths are determined for Eve’s pulse manipulation, which are all located in the so-called blue–green band. The derived successful criterion shows that both attack schemes can control the estimated excess noise of Alice and Bob close to zero by selecting the corresponding condition parameters based on channel transmittance. Additionally, our numerical analysis shows that Eve can steal more bits when the wavelength attack controls the value of the estimated excess noise closer to zero. Full article

In the field of underwater acoustics, the signal-to-noise ratio (SNR) is generally low, and the underwater environment is complex and variable, making target azimuth estimation highly challenging. Traditional model-based subspace methods exhibit significant performance degradation when dealing with coherent sources, low SNR, and small snapshot data. To overcome these limitations, an improved model based on SubspaceNet, called PConv-GAM Residual SubspaceNet (PGR-SubspaceNet), is proposed. This model embeds the global attention mechanism (GAM) into residual blocks that fuse PConv convolution, making it possible to capture richer cross-channel and positional information. This enhancement helps the model learn signal features in complex underwater conditions. Simulation results demonstrate that the underwater target azimuth estimation method based on PGR-SubspaceNet exhibits lower root mean square periodic error (RMSPE) values when handling different numbers of narrowband coherent sources. Under low SNR and limited snapshot conditions, its RMSPE values are significantly better than those of traditional methods and SubspaceNet-based enhanced subspace methods. PGR-SubspaceNet extracts more features, further improving the accuracy of direction-of-arrival estimation. Preliminary experiments in a pool validate the effectiveness and feasibility of the underwater target azimuth estimation method based on PGR-SubspaceNet. Full article

Due to the open underwater channels and untransparent network deployment environments, underwater acoustic networks (UANs) are more vulnerable to hostile environments. Security research is also being conducted in cryptography, including authentication based on asymmetric algorithms and key distribution based on symmetric algorithms. In recent years, the advancement of quantum computing has made anti-quantum attacks an important issue in the field of security. Algorithms such as lattice and SPHINCS+ have become a research topic of interest in the field of security. However, within the past five years, few papers have discussed security algorithms for UANs to resist quantum attacks, especially through classical algorithms. Some existing classical asymmetric and symmetric algorithms are considered to have no prospects. From the perspective of easy deployment in engineering and anti-quantum attacks, our research focuses on a comprehensive lightweight security framework for data protection, authentication, and malicious node detection through the Elliptic Curve and Hash algorithms. Our mechanism is suitable for ad hoc scenarios with limited underwater resources. Meanwhile, we have designed a multi-party bit commitment to build a security framework for the system. A management scheme is designed by combining self-certifying with the threshold sharing algorithm. All schemes are designed based on certificate-less and ad hoc features. The proposed scheme ensures that the confidentiality, integrity, and authentication of the system are well considered. Moreover, the scheme is proven to be of unconditional security and immune to channel eavesdropping. The resource and delay issues are also taken into consideration. The simulations considered multiple variables like number of nodes, attackers, and message length to calculate proper values that can increase the efficiency of this scheme. The results in terms of delay, delivery ratio, and consumption demonstrate the suitability of the proposal in terms of security, especially for malicious node detection. Meanwhile, the computational cost has also been controlled at the millisecond level. Full article

Real-time DOA (direction of arrival) estimation of surface or underwater targets is of great significance to the research of marine environment and national security protection. When conducting real-time DOA estimation of underwater targets, it can be difficult to extract the prior characteristics of noise due to the complexity and variability of the marine environment. Therefore, the accuracy of target orientation in the absence of a known noise is significantly reduced, thereby presenting an additional challenge for the DOA estimation of the marine targets in real-time. Aiming at the problem of real-time DOA estimation of acoustic targets in complex environments, this paper applies the MEMS vector hydrophone with a small size and high sensitivity to sense the conditions of the ocean environment and change the structural parameters in the adaptive adjustments system itself to obtain the desired target signal, proposes a signal processing method when the prior characteristics of noise are unknown. Theoretical analysis and experimental verification show that the method can achieve accurate real-time DOA estimation of the target, achieve an error within 3.1° under the SNR (signal-to-noise ratio) of the X channel of −17 dB, and maintain a stable value when the SNR continues to decrease. The results show that this method has a very broad application prospect in the field of ocean monitoring. Full article

The development of intelligent oceans requires exploration and an understanding of the various characteristics of the oceans. The emerging Internet of Underwater Things (IoUT) is an extension of the Internet of Things (IoT) to underwater environments, and the ability of IoUT to be combined with deep learning technologies is a powerful technology for realizing intelligent oceans. The underwater acoustic (UWA) communication network is essential to IoUT. The thermocline with drastic temperature and density variations can significantly limit the connectivity and communication performance between IoUT nodes. To more accurately capture the complexity and variability of ocean remote sensing data, we first sample and analyze ocean remote sensing datasets and provide sufficient evidence to validate the temporal redundancy properties of the data. We propose an innovative deep learning approach called Ocean-Mixer. This approach consists of three modules: an embedding module, a mixer module, and a prediction module. The embedding module first processes the location and attribute information of the ocean water and then passes it to the subsequent modules. In the mixing module, we apply a temporal decomposition strategy to eliminate redundant information and capture temporal and channel features through a self-attention mechanism and a multilayer perceptron (MLP). The prediction module ultimately discerns and integrates the temporal and channel relationships and interactions among various ocean features, ensuring precise forecasting. Numerous experiments on ocean temperature and salinity datasets show that Mixer-Ocean performs well in improving the accuracy of time series prediction. Mixer-Ocean is designed to support multi-step prediction and capture the changes in the ocean environment over a long period, thus facilitating efficient management and timely decision-making for innovative ocean-oriented applications, which has far-reaching significance for developing and conserving marine resources. Full article