This paper proposed an integrated waveform design and parameter optimization method to realize real-time detection and communication for inverted echo sounders (IES). The approach balances detection performance and communication quality in active sonar systems. An integrated detection– communication waveform (GIDC) is developed using the orthogonality and parameter diversity of generalized sinusoidal frequency modulation (GSFM). The waveform incorporates differential binary phase-shift keying (DBPSK) modulation and superimposed orthogonal GSFM signals, enabling simultaneous acoustic detection and data transmission. To enhance performance, parameter optimization constraints are formulated for the carrier GSFM based on quantitative ambiguity function (AF) analysis and bit error rate (BER) metrics, and are solved using an improved genetic algorithm (GA). The optimization effectively suppresses autocorrelation sidelobes, improves reverberation suppression, and ensures reliable error rate performance. The simulation results on Gaussian and vertical underwater acoustic channels confirm that the proposed waveform achieves robust joint detection and communication. Compared with conventional waveforms, the GIDC exhibits higher detection-delay accuracy and lower BER, demonstrating excellent integrated performance suitable for underwater applications.

In the Internet of Underwater Things (IoUT), mobile edge nodes serve as critical components for data harvesting and network relaying in expansive marine environments. However, their high-mobility nature induces severe Doppler shifts in OFDM-based underwater acoustic links, destroying subcarrier orthogonality and causing strong inter-carrier interference (ICI). Meanwhile, the operation of onboard propulsion systems generates significant non-Gaussian impulsive noise, further under-mining link reliability. This paper proposes a robust channel estimation framework termed Robust Dynamic Cluster-Sparse Bayesian Learning (RDC-SBL). By integrating the Complex Exponential Basis Expansion Model (CE-BEM) with a State Space Model (SSM), RDC-SBL utilizes Inverse-Wishart (IW) and Student-tpriors to track dynamic cluster evolution and adaptively suppress impulsive interference. To enable real-time edge intelligence on resource-constrained IoUT nodes, we further develop RDC-Mamba, a lightweight network distilled from RDC-SBL. By exploiting the structural isomorphism between the Mamba architecture and physical SSMs, RDC-Mamba approximates complex Bayesian posterior inference with linear-time complexity. Simulation results demonstrate that RDC-SBL achieves a 3 dB NMSE gain and reduces the BER to 8 × 10−4under strong interference. Crucially, RDC-Mamba slashes the single-frame inference time from 1.52 s to 12 ms, effectively overcoming the computational bottleneck of onboard processors to satisfy the strict low-latency requirements of dynamic IoUT networks. Sea trials in the South China Sea and the Yellow Sea further validate the framework’s effectiveness for practical underwater engineering applications.

Subsea pipelines are prone to exposure due to natural factors such as earthquakes and vortices, which necessitates regular condition monitoring. Multi-beam echo sounders (MBES) can provide high-precision seabed topographic information, while side-scan sonar (SSS) excels at capturing high-resolution seabed texture features. The integration of these two data sources can complement each other, thereby improving the detection accuracy of subsea pipelines. To achieve effective fusion, high-precision spatial registration is required. However, existing registration algorithms still face challenges such as uneven feature point distribution, dependence on prior knowledge, and unstable matching. This paper proposes a multi-source sonar image registration algorithm for subsea pipelines, named A Hierarchical Feature Structure-Driven Method for Multi-Source Sonar Image Registration of Subsea Pipelines (HFSM). First, the method designs a grid-based multi-scale corner detection (MS-CD), which effectively enhances the spatial distribution balance of feature points. Next, a multi-window geometric-texture joint feature descriptor (MW-GTD) is proposed, which combines direction-sensitive curvature and spatial shadow distribution features within different scale windows. Finally, a multi-layer coarse-to-fine guided matching strategy (ML-CFGM) is introduced to enhance the matching stability of images in feature-sparse regions and realize multi-layer feature matching. The superiority of the proposed method is validated with real-world data, providing technical support for the efficient registration of MBES and SSS images and subsea pipeline detection.

Centralized federated learning is being widely researched and applied. However, centralized federated learning is prone to problems such as single point of failure and privacy disclosure because it relies too much on the central server. Focusing on decentralized federated learning, this paper innovatively constructs a decentralized federated learning framework based on dynamic selection collaboration and topology optimization. Firstly, we propose a dynamic client selection algorithm based on node training quality. Then, a global network topology for data communication is constructed by us based on the Watts-Strogatz(WS) model. Finally, we design a temporary topology algorithm to realize synchronization and model update in training. In the process of decentralized federated learning, the global network topology based on WS model cooperates with the current network topology constructed by temporary topology algorithm. The two network topologies work together to realize a dynamic client selection algorithm based on node training quality. A large number of experiments verify that DFedMQ can accelerate the model convergence and improve the training effect under the premise of privacy protection.

Deep reinforcement learning models have achieved promising results in the path planning problem for uncrewed aerial vehicles (UAVs). However, their update mechanisms can lead to overestimation and poor stability. This study addresses these issues by using a more realistic reward function, assigning priorities to experiences in the experience replay pool, and employing double decoupling of state and action values in Q-networks. We train the improved Deep Q-Network (DQN) algorithm for three-dimensional environment simulation experiments in a simulated environment. The 3D simulation experiments compare the algorithm with the A-star and unimproved DQN algorithms. The experimental results show that the algorithm has been improved, demonstrating its enhanced performance in the final path planning results. Moreover, the final testing results reveal that the UAV can safely reach the target point from the starting point.

With the development of Internet of Medical Things(IOMT), a lot of medical data are stored and released for both scientific research and practical applications. Accurate medical data is very valuable, but it also brings a huge risk of privacy leakage. Moreover, improving the privacy of data often leads to the reduction of data validity. Privacy and effectiveness are in conflict, and their balance is a typical multi-objective optimization problem (MOP). In this paper, we try to use differential privacy to disturb medical data to protect personal privacy. We propose the Environment Switching Algorithm (ESA) based on evolutionary learning to solve this MOP. ESA has excellent performance, which can ensure convergence speed and optimization performance at the same time. The result of optimization is a pareto front (PF) of huge scale, which includes solutions with different characteristics. We put forward a method of double clustering to select the appropriate solution from PF. Based on the above, we conclude the whole method as Flexible Differential Privacy Algorithm based on Evolutionary Learning (FDPEL). FDPEL can realize flexible differential privacy for medical data, while ensuring data privacy and data validity. FDPEL is suitable for privacy protection of medical data of different scales, which makes it have a practical applications value.

Automatic modulation recognition (AMR) of underwater acoustic (UWA) signals is incredibly challenging due to the complexity of UWA channels and the severity of ocean noise. In the presence of noise interference, single-modal features fail to fully represent the characteristics of different modulated signals. While the in-phase/quadrature (I/Q) and time-frequency maps can adequately represent the signal features in the time, frequency, and time-frequency domains, the direct integration of the two modalities is ineffective because of the variations in shape, information granularity, and noise manifestation. To address the low recognition rate caused by the above issues, we propose a two-stream transformer (TSTR) based network for AMR of UWA signals. First, the input pre-processing layer obtains the I/Q and time-frequency features from the received signals. Then, the feature capture layer extracts high-dimensional signal features in the time, frequency, and time-frequency domains. Finally, the classification layer estimates the modulation of the signals. A multi-head self-attention module with adaptive soft thresholding is used in the feature capture layer to provide noise reduction and redundant feature rejection while retaining context information. Moreover, multi-scale ghost convolution is employed to address the inability of the transformer to efficiently extract spatial characteristics from the signals. Results are presented using real UWA channels from the Watermark dataset for two different seas which show that the TSTR improves recognition by 1.2% and 5.9% over the best existing model. Further, it has better generalization capabilities and the model has a small number of parameters so the time complexity is low.

The Transformer is one of the mainstream methods in computer vision. Most Transformer based architectures focus on the design of spatial attention and optimizing the computational complexity of high resolution of pixels in image but pay little attention to modeling channel dependencies and optimizing the computational complexity associated with a large number of channels. In this paper, we propose a new channel window based self-attention mechanism and apply two consecutive transformer layers to capture global channel dependencies through permuting channel layer, which can greatly reduce the computational complexity caused by a large number of channels. Meanwhile, a new linear layer for channel attention is proposed, which eliminates the need for position bias in Transformer. The proposed method can be conveniently appended to the existing image classification architectures in parallel with minimal modification. We demonstrate the feasibility of the proposed method on the state-of-the-art transformer-based architecture for image classification and improve the results on ImageNet-1K. The code will be publicly available at GitHub.

In recent years, in-vehicle applications based on vehicular ad hoc networks (VANETs) have been continuously expanded. Many applications not only focus on delay and effective forwarding rate but also pay more attention to routing path multiplexing and throughput. However, in VANETs, it is challenging to establish real-time and robust multihop forwarding paths due to volatile topological information, disconnected network, churn rate, etc. In order to adapt to the new development trend of VANETs, a self-healing routing strategy (SR) with the ant colony optimization (ACO) is proposed in this article. SR introduces the ACO algorithm to establish routing paths to ensure connectivity and immediacy. The routing-build-ability (RBA) is defined to measure the forwarding capability of a vehicle. The RBA is derived from the delay and packet delivery ratio (PDR) using the fuzzy logic system, which can reduce the computational complexity. To reduce the overhead of path reconstruction performed due to path disconnection, in-road-repairing and intersection-repairing methods are proposed in this article, which prolong the duration of the optimal path and improve throughput. The simulation results and mathematical analyses demonstrate that the feasible SR can reduce the delay by 30%, shorten the time overhead to one sixth, promote the routing duration by three times, and enhance the throughput by three times.

Image and sentence matching has attracted increasing attention since it is associated with two important modalities of vision and language. Previous methods aim to find the latent correspondences between image regions and words by aggregating the similarities of the region-word pairs. However, these approaches consider little about the relationships of diverse regions in the image and treat the similarities of all region-word pairs equally. Moreover, focusing on fine-grained alignment overly, the true meaning of the original image will be likely distorted. In this paper, a novel Region Reinforcement Network with Topic Constraint (RRTC) is proposed to explore the correspondences between images and texts. Specifically, the region reinforcement network is built to infer fine-grained correspondence by considering the relationships of regions and re-assigning region-word similarities. Meanwhile, the topic constraint module is presented to summarize the central theme of images, which constrains the original image deviation. Extensive experimental results on MSCOCO and Flickr30k datasets verify the effectiveness of our proposed RRTC.