Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs.

In fiber Bragg grating (FBG) sensing demodulation systems, high-precision peak detection is a core requirement for demodulation algorithms. However, practical spectral signals are often susceptible to environmental noise interference, which leads to significant degradation in the accuracy of traditional demodulation methods. This study proposes a self-adaptive K-SVD (SAK-SVD) denoising algorithm based on adaptive window parameter optimization, establishing a closed-loop iterative feedback mechanism through dual iterations between dictionary learning and parameter adjustment. This approach achieves a synergistic enhancement of noise suppression and signal fidelity. First, a dictionary learning framework based on K-SVD is constructed for initial denoising, and the peak feature region is extracted by differentiating the denoised signals. By constructing statistics on the number of sign changes, an adaptive adjustment model for the window size is established. This model dynamically tunes the window parameters in dictionary learning for iterative denoising, establishing a closed-loop architecture that integrates denoising evaluation with parameter optimization. The performance of SAK-SVD is evaluated through three experimental scenarios, demonstrating that SAK-SVD overcomes the rigid parameter limitations of traditional K-SVD in FBG spectral processing, enhances denoising performance, and thereby improves wavelength demodulation accuracy. For denoising undistorted waveforms, the optimal mean absolute error (MAE) decreases to 0.300 pm, representing a 25% reduction compared to the next-best method. For distorted waveforms, the optimal MAE drops to 3.9 pm, achieving a 63.38% reduction compared to the next-best method. This study provides both theoretical and technical support for high-precision fiber-optic sensing under complex working conditions. Crucially, the SAK-SVD framework establishes a universal, adaptive denoising paradigm for fiber Bragg grating (FBG) sensing. This paradigm has direct applicability to Raman spectroscopy, industrial ultrasound-based non-destructive testing, and biomedical signal enhancement (e.g., ECG artefact removal), thereby advancing high-precision measurement capabilities across photonics and engineering domains.

To address the issues of poor environmental adaptability and high costs associated with traditional methods of measuring water body boundaries, this paper proposes an innovative path planning approach for water body boundary measurement based on Unmanned Surface Vehicles (USVs)—the Chemotactic Search Traversal (CST) algorithm. This method incorporates the chemotaxis operation mechanism of the Bacterial Foraging Optimization algorithm, integrating it with high-resolution grid maps to enable efficient traversal and accurate measurement of water body boundaries within large-scale grid environments. Simulation experiments demonstrate that the CST algorithm outperforms the Brute Force Algorithm (BFA), Roberts operator, Canny operator, Log operator, Prewitt operator, and Sobel operator in terms of optimal pathfinding, stability, and path smoothness. The feasibility and reliability of this algorithm in real water environments are validated through experiments conducted with actual USVs. These findings suggest that the CST algorithm not only enhances the accuracy and efficiency of water body boundary measurement but also offers a cost-effective and practical solution for measuring water body areas.

Fibre Bragg Grating (FBG) demodulation technology is central to structural health monitoring. However, spectral distortion and noise caused by complex environments, along with the challenge of balancing accuracy and real-time performance in existing deep learning algorithms, severely limit its application in large-scale dynamic sensing networks. To address this challenge, this paper proposes a hybrid learning framework named TimeMixer-LightGBM. The framework first employs a pure MLP-based TimeMixer model to efficiently extract multi-scale spectral sequence features from FBG reflection spectra, and then uses a LightGBM model (gradient boosted decision trees) to perform fast regression on these features for centre wavelength shift prediction. Experiments on synthetically distorted FBG spectra (including asymmetric shape variations and additive white noise) show that the method achieves picometre-level accuracy (RMSE = 2.128 pm) with an average processing time of only 0.08 ms per spectrum, representing a speedup of about 4.5 times over the latest deep learning models for FBG demodulation. It also exhibits excellent noise robustness, maintaining an average absolute error of 1.5 pm. Ablation experiments confirm the necessity and synergy of the hybrid architecture. The framework was further applied to demodulate double-peaked overlapping spectra, outperforming existing methods under mild, moderate and severe overlap conditions while keeping inference times in the sub-millisecond range. This study provides a novel and effective technical solution for real-time high-precision FBG demodulation, validates the effectiveness of pure MLP architectures in spectral analysis, and lays a theoretical foundation for deploying such demodulation on embedded edge devices, thereby achieving a favourable balance of accuracy, speed and scalability.