Reliable underwater adhesion is a prerequisite for the practical application of hydrogels as underwater coatings. Despite extensive efforts to enhance adhesion strength, strong initial adhesion does not ensure long-term adhesion stability. Achieving durable underwater adhesion of hydrogels remains fundamentally challenging due to interfacial hydration and continuous water-induced network degradation, particularly in complex aqueous environments such as seawater. To overcome these limitations, a hydrogel coating that achieves persistent underwater adhesion in natural seawater for over 900 days is developed. Strong initial adhesion is established across diverse substrates through synergistic interfacial interactions, while network integrity is maintained during prolonged immersion through multiple cooperative intermolecular interactions and continuous quinone-mediated oxidative crosslinking. Consequently, the hydrogel exhibits persistently low water uptake (≈162 wt% after 900 days), suppresses swelling-induced deterioration of interfacial adhesion, and maintains a shear adhesion strength of ≈110 kPa after 900 days of seawater immersion. The hydrogel coating also exhibits antibacterial and anti-algal activity for marine antifouling, underwater superoleophobicity, and applicability as a gel electrolyte for flexible supercapacitors. This work establishes a design principle for achieving long-term underwater functional hydrogels beyond previously reported time scales and provides a general strategy for developing continuously curing materials in underwater and marine environments.
Wearable flexible sensors for underwater communication and biomotion monitoring are gaining attention. However, developing gel-based strain sensors with high toughness, anti-swelling performance, robust underwater adhesion, and long-term stability remains challenging. Herein, we present a hydrophobic eutectogel (DPF-Zn@LNP-HEG) fabricated via the assembly of a polymerizable hydrophobic deep eutectic solvent (PHDES), 2-phenoxyethyl acrylate (PEA), and Zn2+-coordinated lignin nanoparticles (Zn@LNP). The resulting structure, composed of hydrophobic polymer networks and metal-phenolic complexes, forms hydrophobic microdomains that disrupt the hydration layer and prevent water penetration. Meanwhile, Zn@LNP serve as dynamic sacrificial cross-linkers, enhancing the material's mechanical strength, energy dissipation, and anti-swelling properties. The resulting eutectogel exhibits remarkable tensile strength (1.14 MPa), superior toughness (3.15 MJ m-3), excellent anti-swelling properties (< 1% after 30 days), and robust underwater adhesion (1.07 MPa on glass). Based on these properties, we demonstrate an underwater strain sensor with high sensitivity (gauge factor = 8.12) and long-term stability, enabling underwater Morse code transmission, biomotion monitoring, and Bluetooth-based tracking of swimming movements. This work not only provides a versatile design paradigm for multifunctional underwater sensing platforms but also advances the high-value utilization of bio-based materials in next-generation flexible electronics.
The underwater environment is complex and ever-changing, with light being easily absorbed and scattered, resulting in blurred and color-shifted images. This poses challenges for target detection, often leading to false positives and missed detections. To enhance the accuracy and stability of underwater target detection, an improved YOLO11 algorithm is proposed. First, the C3k2 module is enhanced by introducing a Dynamic Feature Fusion (DFF) mechanism, resulting in the C3k2-DFF module. Leveraging its dynamic weighting capability, this module enhances the representation of multi-scale features, enabling more precise localization of underwater targets. Second, by introducing a Global Context (GC) mechanism to enhance the Visual Feature Enhancement (VFE) module, the VFE-GC module is designed to adaptively recalibrate features using global information, suppressing background noise while amplifying key semantic information of targets. Finally, the Shape-IoU loss function is improved by incorporating the Normalized Wasserstein Distance (NWD), resulting in the Shape-NWD loss function. The NWD loss function optimizes bounding box regression by enforcing shape consistency constraints, thereby enhancing localization accuracy for irregular or elongated underwater targets. Results show that DVS-YOLO11 achieves an mAP@0.5 of 84.78% and an mAP@0.5:0.95 of 64.67%, along with 87.44% Precision and 75.57% Recall, while maintaining low computational costs. Notably, for scallop targets characterized by blurry edges and irregular shapes, the mAP@0.5 improved from 63.9% to 68.6%. Compared with the baseline YOLO11, all metrics show improvements, indicating that the proposed method works well for underwater target detection.
Aiming at the problem that sparse channel estimation in underwater acoustic communication is susceptible to complex multipath propagation, non-Gaussian impulsive noise, and channel time variations, this paper proposes a bidirectional proportionate recursive maximum correntropy criterion algorithm, referred to as Bi-PRMCC. By introducing a bidirectional filtering structure into the proportionate recursive maximum correntropy criterion (PRMCC) framework, the proposed algorithm jointly exploits the information from forward and backward data sequences, thereby improving the estimation accuracy and block-based channel variation tracking capability for sparse underwater acoustic channels. Meanwhile, the maximum correntropy criterion enhances the robustness of the algorithm against non-Gaussian impulsive noise and outlier error samples, while the proportionate update mechanism improves its identification capability for dominant taps in sparse channels. To verify the effectiveness of the proposed algorithm, short-range sparse underwater acoustic channels and long-range complex multipath underwater acoustic channels are constructed based on the Bellhop ray-tracing model. Simulation experiments are then conducted under three typical non-Gaussian noise environments, namely Cauchy noise, α-stable distribution noise, and Middleton noise. The experimental results show that, compared with recursive least squares (RLS), bidirectional recursive least squares (Bi-RLS), proportionate recursive least squares (PRLS), recursive maximum correntropy criterion (RMCC), and PRMCC, Bi-PRMCC achieves a lower steady-state normalized mean square deviation (NMSD) under different non-Gaussian noise conditions, indicating stronger robustness against impulsive noise. Under different signal-to-noise ratio conditions, the proposed algorithm still maintains superior steady-state estimation performance. In addition, in the channel abrupt-change tracking experiment, Bi-PRMCC can rapidly reconverge after channel variations occur, demonstrating favorable reconvergence capability under abrupt channel variations. The ablation study further verifies the stable performance gain brought by the bidirectional structure to PRMCC. Overall, the proposed Bi-PRMCC algorithm exhibits high estimation accuracy, robustness, and reconvergence capability under complex non-Gaussian noise and abrupt channel variation conditions.
Conductive hydrogel fibers are ideal for wearable underwater sensing due to their weavability, conformal deformation adaptability, and ionic conductivity. However, conventional hydrogel fibers swell severely in water, causing mechanical deterioration, disrupted conductive pathways and signal instability, which limits their underwater applications. Herein, dual-network polyvinyl alcohol (PVA)/sodium alginate (SA)/glutaraldehyde (GA) (PSG) organohydrogel fibers were fabricated by combining wet-spinning, freeze-thawing and solvent replacement. The covalently crosslinked PVA-GA network acts as a stable structural skeleton, the ionically coordinated SA-Ca2+ network serves as reversible sacrificial bonds for energy dissipation, and freeze-induced PVA microcrystals enhance crosslinking density to restrain swelling. The glycerol/water binary solvent system endows the fibers with excellent low-temperature tolerance. The resultant fibers exhibit a tensile strength of 2.32 MPa, elongation at break of 610%, an ultra-low 7 day swelling ratio of 4.95%, wide-temperature conductivity from -25 °C to 60 °C, and outstanding strain-sensing performance with a maximum gauge factor of 3.53, 150 ms response time and stable operation over 1500 loading-unloading cycles. They can monitor full-scale human motions, and combined with bidirectional long short-term memory deep learning, achieve an underwater Morse code communication system with 97.3% recognition accuracy for 26 English letters, supporting wearable sensing in underwater scenarios.
This paper addresses the problem of controlling autonomous underwater vehicles (AUVs) operating in a swarm under realistic underwater conditions characterised by inaccurate navigation, limited acoustic communication, and noisy sonar observations. A novel Trail Sonar-Based Algorithm (TSBA) is proposed for leader-follower swarm control. Unlike conventional reactive approaches, TSBA combines sparse acoustic communication with prior knowledge of the mission plan, enabling predictive estimation of the tracked vehicle's state and reducing the dependence on continuous information exchange. To evaluate its effectiveness, TSBA was compared with a machine learning-based controller (NSCSUV) in a simulation environment incorporating navigation drift, sonar measurement errors, and a data-driven model of a real low-cost AUV. The proposed vehicle model achieved a mean speed error of 0.107 m/s and a mean heading error of 14.25°, providing a realistic basis for controller evaluation. Simulation results demonstrated that TSBA consistently outperformed the neural network-based approach in formation keeping while generating smoother control commands and requiring only minimal underwater communication. The algorithm maintained stable swarm behaviour despite sensor inaccuracies and communication constraints. Finally, experiments conducted with a real underwater vehicle confirmed the practical applicability and robustness of the proposed approach under real operating conditions.
Underwater object detection is a fundamental task in underwater sensing and is generally approached using either optical or sonar sensors. Although optical imaging provides rich semantic information, it is highly susceptible to water turbidity and illumination variations. By contrast, sonar imaging can effectively overcome visibility limitations, yet it suffers from severe speckle noise and blurred object contours. Moreover, resource-limited platforms impose strict demands on model lightweightness and real-time performance. To this end, this paper proposes a novel cross-modal heterogeneous distillation method (CMHD) to balance detection accuracy and computational complexity. CMHD performs cross-modal knowledge transfer by leveraging the rich semantics of RGB images to enhance sonar feature representation, compensating for the information deficiency of the sonar modality. Meanwhile, a heterogeneous distillation scheme compresses the detection capability of a high-capacity teacher YOLOX-M into a lightweight student YOLOX-S-Ghost, enabling strong feature extraction under a highly compact model. To mitigate the modality gap and geometric inconsistency between RGB and sonar modalities, we design a branch-aware heterogeneous distillation strategy. To improve detection accuracy and reduce model parameters, the student network incorporates Coordinate Attention (CA) in its backbone and adopts a lightweight neck design. Experiments on the UXO† dataset demonstrate that CMHD achieves 79.6% mAP and 82.6% mAR, significantly outperforming the compared representative methods and serving as an accurate, efficient, and lightweight solution for underwater sonar object detection.
Biomimetic underwater acoustic communication (BUAC) is a key technology for underwater data transmission, yet existing BUAC techniques are hindered by low data rates and inadequate degree of mimic (DoM). Inspired by the wideband click trains of dolphins, this paper proposes a high‑speed BUAC scheme that employs dolphin clicks as information carriers. Information is embedded by jointly modulating the amplitude and phase of these clicks. The effects of Doppler shift and inter‑symbol interference (ISI) are effectively mitigated through the cascaded application of interleaving, Gray coding, resampling, and channel equalization. Furthermore, handshake‑free adaptive modulation is implemented via the frame header, ultimately achieving high‑speed and reliable BUAC. Shallow‑water sea trial results demonstrate that the proposed method achieves a communication performance of 22.54 kbps*km with a bit error rate of only 0.0019, which is twice the upper bound of existing techniques. Moreover, the technique can flexibly switch its signal carrier according to local biological signals, further enhancing DoM and providing a feasible solution for high‑speed BUAC among underwater equipment.
Underwater optical devices are indispensable for marine scientific research, resource exploration, national defense, and environmental monitoring. However, marine biofouling poses a significant threat to underwater optical devices, as it reduces monitoring accuracy and impairs image clarity. Transparent marine antifouling coatings, which offer an effective means of balancing high optical transmittance with reliable protection for marine optical devices, have emerged as a focal point of research. This review summarizes recent advances in the structural design and functionalization of transparent marine antifouling coatings. First, the dynamic evolution of marine biofouling is described, and the optical failure starting point of underwater optical devices and its key influencing factors are clarified. Then, the key performance indicators of transparent antifouling coatings are systematically summarized. Meanwhile, the antifouling mechanisms are examined in depth. Subsequently, design strategies for transparent marine antifouling coatings are comprehensively discussed, including wettability regulation strategies, biocide-releasing strategies, bioinspired design, intelligent response, and composite design strategies. Finally, the key challenges faced by current transparent antifouling strategies are identified, and future research directions are proposed. This work provides a comprehensive and valuable reference for the future development and practical application of transparent marine antifouling coatings.
The paper addresses the problem of maritime navigation in coastal areas without access to satellite positioning data. The proposed approach relies on visual information from a monocular camera supported by map-derived coastal information. Although the operational concept assumes the use of publicly available cartographic data, the method's preparation and evaluation require additional processing steps, including high-resolution aerial imagery, GIS-based extraction of coastal features, semantic segmentation, and trained convolutional neural network models. The problem discussed in the paper concerns, for example, Autonomous Underwater Vehicles that seek to reduce underwater dead-reckoning navigation error by surfacing and using information about what is visible around them, in a way similar to how a human would. To solve the above problem, a system was proposed that compares the camera's representation of the observed coastline with the map representation of the area where the vehicle is most likely located. The system was validated using real-world data. The tests revealed that the information contained in a flat map is insufficient for accurate position estimation. Accuracy is also significantly affected by errors in the semantic segmentation used to extract land features from camera images, as well as by potential errors in the camera viewing angle. The achieved accuracies are sufficient for navigation away from land, but when operating close to land, the proposed system appears significantly insufficient. The paper specifies the system and reports the results.
The advancing perception capabilities of an individual unmanned underwater vehicle (UUV) pose new challenges for multi-target perceptual consistency in underwater bearing-only tracking (BOT) systems. Accurate target state estimation necessitates two key prerequisites: sensor bias compensation and precise data association. The biases encompass both sensor spatial bias and signal propagation delay between the target and sensor. This paper introduces a measurement model that explicitly accounts for these factors. To address the lack of prior target information, an initial target state estimation algorithm is developed based on maximum likelihood estimation (MLE), with a refined bio-inspired variant incorporating particle swarm optimization (PSO). To this end, a cost function is formulated to transform the BOT data association problem into an assignment problem. Thereafter, an iterative multi-target data association (MDA) algorithm, integrated with the expectation-maximization (EM) method, is designed to jointly mitigate the effects of signal delay and spatial bias. Monte Carlo simulation scenarios validate the overall effectiveness of the proposed MDA framework. Specifically, the EM-based spatial bias estimation method demonstrates accurate bias estimation capability.
Hybrid nanogenerators that combine piezoelectric and triboelectric mechanisms provide an effective strategy for harvesting mechanical energy from aquatic environments, where device durability and biofouling resistance remain significant challenges. In this work, we report a flexible hybrid nanogenerator based on electrospun poly(vinylidene fluoride) (PVDF) nanofibers incorporated with aluminium nitride (AlN) nanoparticles and encapsulated with hydrophobic Ecoflex for stable underwater operation. The incorporation of AlN acts as an efficient nucleating agent that enhances the electroactive β-phase formation and crystallinity of PVDF nanofibers without external poling. Systematic variation of AlN loading shows that 1 wt% AlN yields the highest crystallinity and β-phase content. Structural and electromechanical characterisations confirm improved dipole alignment and mechanical flexibility of the nanofiber mats, establishing a clear structure-property-performance relationship between nanocomposite design, piezoelectric polarisation, and electrical output. The hybrid nanogenerator integrates the PVDF/AlN piezoelectric layer with a Kapton-based triboelectric unit and is encapsulated with compliant Ecoflex to ensure waterproofing and mechanical stability. Under periodic finger tapping in air, the device produces an open-circuit voltage of 58 V and a short-circuit current of 6 μA, outperforming individual piezoelectric nanogenerator (PENG) and triboelectric nanogenerator (TENG) components. When operated as a cantilever under flowing water, the device generates 85 V and 20 μA at a flow velocity of 2 m/s, delivering a maximum instantaneous power of 0.2 mW and a power density of 0.066 W m-2 at a load resistance of 1 MΩ. The nanogenerator powers 30 blue light-emitting diodes (LEDs) and demonstrates hydrophobicity; the encapsulated device exhibited reduced bacterial adhesion from Staphylococcus aureus and Pseudomonas aeruginosa, indicating improved interfacial stability and suitability for long-term underwater operation. These results demonstrate a scalable and robust nanogenerator architecture for durable underwater energy harvesting and marine sensing.
Underwater objects often suffer from severe visual degradation, including strong background noise induced by water impurities, blurred boundaries caused by optical scattering, and small object suppression in complex environments. These factors collectively pose significant challenges to underwater object detection (UOD). To address these issues, we propose the Multi-Scale Inverted Pyramid Network (MIP-Net) tailored for UOD. Moving beyond standard feature fusion paradigms, MIP-Net introduces explicit conceptual shifts through two key components: the Local Adaptive Contrast module (LAC) and the Multi-Scale Inverted Feature Pyramid Network (MSIFPN). Unlike conventional global attention mechanisms, LAC selectively calibrates intra-feature contrast layer-by-layer, establishing a theoretical basis for dynamic feature modulation that prevents high-frequency detail dilution. Simultaneously, MSIFPN transcends traditional FPN architectures by coupling a dual-pyramid bidirectional flow with a strict mathematical foreground-background separation strategy, effectively isolating target semantics from ambiguous water impurities. Experiments on the official DUO benchmark demonstrate that MIP-Net achieves an AP of 70.1%, surpassing state-of-the-art single-stage, two-stage, and Transformer-based methods, while maintaining a highly competitive trade-off between computational efficiency (Params/FLOPs) and accuracy. Furthermore, evaluations on the terrestrial COCO dataset yield an AP of 45.6%, confirming the strong cross-domain generalization capability of our framework. The code is publicly available at: https://github.com/YitengGuo/MIP-Net.
Biological systems have long inspired the design of functional materials, yet achieving functionally decoupled integration of multiple biomimetic functionalities within a single platform, especially across different media, remains a major challenge. Here, we report a jellyfish-coral bioinspired bilayer architecture that enables infrared (IR, 2.5-25 µm) stealth and electromagnetic (EM, 2-18 GHz) shielding above water as well as acoustic stealth (1-10 kHz) and drag reduction underwater. The upper layer, inspired by jellyfish microvilli, comprises a microvillar Si nanoarray that exhibits ultra-low infrared emissivity (< 20%) and air-film-mediated drag reduction, as evidenced by a water contact angle of 108.64°. The lower layer, inspired by coral skeletons, consists of a porous SiC framework decorated with in situ grown nanowire networks, which delivers broadband electromagnetic absorption (minimum reflection loss of -56.02 dB and an effective absorption bandwidth of 10 GHz at a thickness of only 3 mm) together with efficient acoustic wave dissipation (absorption coefficient ≈ 0.7 at an incident angle of 80°). These two bioinspired layers are monolithically integrated while preserving their respective functionalities, such that each stealth modality operates independently and without detrimental coupling. Consequently, the metastructure achieves robust, multispectral stealth performance in both sea-surface and underwater environments.
Underwater noise generated by operational offshore wind turbines has attracted great concern in marine acoustics and offshore engineering. Existing approaches for evaluating the sound pressure levels highly rely on empirical formulations or computationally intensive finite element models. In this study, a semi-analytical model is developed to provide a quick yet reliable tool for predicting the sound pressure levels from operating turbines. A monopile-supported turbine equipped with a gearbox is considered due to its widespread use in practice. It is carefully assumed that the gear force is the dominant excitation source according to related literature. The seawater and the seabed are modeled as three-dimensional acoustic and elastic media, respectively, while both the tower and monopile are represented using Timoshenko theory. The governing equations are solved using the Laplace transform and separation of variables methods by combining the pile-water-soil coupled interaction. Model validation is demonstrated through comparisons with published studies, including numerical simulations, field measurements, and an empirical model. These cases cover turbines of 1, 1.5, 3.6, and 6 MW, with monitoring distances ranging from 20 to 500 m from the monopile foundation. The results demonstrate that the model provides reasonable predictions of underwater noise generated by operating turbines.
The moving target detection in active sonar measurement is essential for underwater surveillance. Complex reverberation in a shallow-water environment frequently gives rise to false detection and severely degrades system performance. Low-rank and sparse decomposition, exploiting the low-rank characteristic of reverberation and the sparse nature of moving target across multi-frame range-bearing images, is currently the mainstream method for reverberation suppression. The performance of the reverberation suppression method based on the optimization framework is affected by the manual selection of the regularization parameter that balances the low-rank reverberation and the sparse moving target. To address the issue, this paper implements reverberation suppression in underwater moving target detection within a Bayesian framework. A hierarchical Dirichlet process with a Gaussian mixture model is developed to characterize the non-low-rank component, which exhibits a non-independent and non-identical distribution across different time frames. The steady reverberation component is modeled using the inherent low-rank structure. Based on the proposed model, variational inference is employed to estimate all involved variables, in which the moving target is effectively extracted from complex reverberation. The superiority and robustness of the proposed method are validated through a field target detection experiment compared with other methods.
A cyber-physical underwater vehicle is equipped with bio-inspired flapping fins positioned on the sides of the vehicle's main body. The proposed control surfaces are inspired by fish pectoral fins, generating forces and moments that can potentially be harnessed for maneuvering, hovering and station keeping. The streamwise and cross-stream forces produced by the fins are characterized for a range of reduced frequencies and Strouhal numbers. The streamwise forces are shown to be predominantly a function of the fin's projected frontal area, while the lateral forces also depend on the Strouhal number. When operated simultaneously, different flapping synchronizations can be employed for specific goals; a symmetric motion suppresses the lateral forces, while an anti-symmetric motion decreases the peaks of the streamwise force produced. The cyber-physical vehicle demonstrates how the pair of fins can successfully maneuver the vehicle in the lateral direction.
We report successful en bloc underwater endoscopic mucosal resection (UEMR) of a lesion involving the minor duodenal papilla. A woman in her 60s presented with a 10-mm depressed lesion in the descending duodenum. White-light imaging and Magnifying endoscopy suggested a tumor involving a minor papilla. Although side-viewing endoscopes are conventionally used for the minor papilla, pre-procedural assessment indicated inadequate maneuverability for this specific lesion (ED-580T; Fujifilm Corporation, Tokyo, Japan). Consequently, we strategically used a forward-viewing dual-channel endoscope (GIF-2TQ260M; Olympus Medical Systems, Tokyo, Japan). Utilizing its right-side instrument channel and independent suction capability provided a direct approach and stable field of view, facilitating precise snaring. Histopathology confirmed a 15 × 13-mm intestinal-type adenoma with negative margins. Given the minor papilla was located at the tumor margin and lacked a luminal orifice, the lesion was diagnosed as a superficial non-ampullary duodenal epithelial tumor (SNADET) secondarily extending to the papilla. Forward-viewing endoscopes are viable alternatives for this region depending on lesion orientation. Furthermore, comprehensive pre-procedural assessment, including evaluation for pancreas divisum, may aid in determining treatment strategies, such as the omission of pancreatic stenting. Strategic, case-specific endoscope selection is paramount for safe therapeutic intervention in this complex anatomical region.
A nonlinear mathematical model describing the vertical motion of a biomimetic underwater vehicle equipped with a swim bladder is developed. For a passive bladder that changes its volume under hydrostatic pressure, the system can achieve depth stabilization through a speed-induced mechanism when the lever-arm geometry (relative positions of the swim bladder and lifting surfaces) is favorable; otherwise stabilization is not possible. Using the Routh-Hurwitz criterion, analytical stability conditions are obtained in closed form, revealing a lower onset speed that is set by a simple coupling between forward speed and geometry. Numerical simulations confirm the theoretical predictions and reveal the dominant loss-of-stability scenarios: loss of effective stiffness at the onset threshold and oscillatory instability when the mixed speed-geometry factor changes sign. The results demonstrate the feasibility of passive swim-bladder-based depth stabilization and provide practical guidelines for selecting the lever arms of the buoyancy and lift forces and operating speeds in autonomous underwater vehicles.
Autonomous Underwater Vehicles (AUVs) face significant challenges in path-following control due to strong environmental disturbances and model uncertainties. To address these issues, this paper proposes a model-free deep reinforcement learning framework, named ILLT (Improved LOS-LSTM-TD3), which integrates an integral line-of-sight (LOS) guidance law with the twin delayed deep deterministic policy gradient (TD3) algorithm. The framework treats the LOS look-ahead distance as a learnable optimization variable and incorporates an LSTM network to capture temporal motion dependencies. A progressive unfreezing transfer learning strategy, combined with attention-based feature-current fusion, is designed to enhance domain adaptation under varying ocean currents. Simulation results demonstrate that ILLT reduces the average cross-track error by 48.5% compared to the baseline ILT algorithm and by 66.4% compared to traditional PID control, while achieving significantly faster convergence in target domains. Physical experiments in tank and lake environments further validate the algorithm's feasibility and robustness, with tracking errors approaching simulation results under moderate current conditions. These findings confirm the effectiveness of the proposed framework for underactuated AUV path-following tasks.