Wearable optical fiber sensors have great potential for development in medical monitoring. With the increasing demand for compactness, comfort, accuracy, and other features in new medical monitoring devices, the development of wearable optical fiber sensors is increasingly meeting these requirements. This paper reviews the latest evolution of wearable optical fiber sensors in the medical field. Three types of wearable optical fiber sensors are analyzed: wearable optical fiber sensors based on Fiber Bragg grating, wearable optical fiber sensors based on light intensity changes, and wearable optical fiber sensors based on Fabry–Perot interferometry. The innovation of wearable optical fiber sensors in respiration and joint monitoring is introduced in detail, and the main principles of three kinds of wearable optical fiber sensors are summarized. In addition, we discuss their advantages, limitations, directions to improve accuracy and the challenges they face. We also look forward to future development prospects, such as the combination of wireless networks which will change how medical services are provided. Wearable optical fiber sensors offer a viable technology for prospective continuous medical surveillance and will change future medical benefits.

Spread spectrum communication technology is characterised by strong anti-interference, strong concealment, multipath resistance and low power spectral density. In recent times, it has been widely used in aerospace measurement and control, military communication, hydroacoustic communication, optics and other fields for the purpose of realising high quality and high speed transmission of signals and completing the purpose of information transfer. In recent years, along with the development of 5G and other communication methods, spread spectrum technology has been introduced into the public's field of view, and it is the focus of future spread spectrum technology to consider how to achieve high speed rate and high applicable communication quality. Firstly, the working principle, composition and development direction of basic spread spectrum technology, hybrid spread spectrum technology and other spread spectrum methods are introduced, such as summarising the fuzzy problems of BOC signals and introducing the improvement of direct sequence spread spectrum technology after the introduction of chaotic sequences. The advantages and limitations of each of the different spread spectrum techniques are analysed for the advantages and disadvantages at the end.

In response to the limitations observed in the Intelligent Water Droplet (IWD) algorithm for path planning, including its weak problem-solving capability and susceptibility to local optimization, this paper presents an enhanced Intelligent Water Droplet algorithm. The improved algorithm incorporates the distance factor between nodes and the target point into the original algorithm’s path probability, enhancing its problem-solving prowess and expediting algorithmic convergence. Simultaneously, a roulette-based probability selection method is introduced to circumvent local optimization during the solution process. Additionally, the algorithm is coupled with the Floyd algorithm to refine the planned path, reducing the number of inflection points to align with the motion characteristics of the inspection robot.Simulation results underscore the effectiveness of the enhanced Intelligent Water Droplet algorithm in mitigating the impact of local optimization in path planning. In comparison to the original IWD algorithm, the optimized path exhibits a 17.42% reduction in length, a 58.3% decrease in the minimum number of iterations required for path convergence, and a 36.3% reduction in the number of path inflection points. Furthermore, the total path length is decreased by 7.7% following path optimization via the Floyd algorithm.

Finger vein recognition has emerged as a highly robust and intrinsically stable biometric technology, demonstrating great potential in identity authentication and intelligent security applications. However, conventional methods still suffer from constraints in feature representation and computational efficiency, particularly under challenging conditions such as illumination variation, pose deviation, and noise interference. To address these challenges, this study presents an efficient finger vein recognition approach based on a lightweight convolutional neural network (LCNN) architecture. The proposed framework integrates a multi-stage image preprocessing pipeline for automatic vein region detection, advanced denoising, and refined texture enhancement, which is subsequently followed by compact feature modeling within a lightweight deep network. Extensive experiments on the public Shandong University Machine Learning and Applications-Homologous Multi-Modal Traits (SDUMLA-HMT) dataset and a self-acquired Laboratory Finger-Vein (Lab-Vein) dataset validate the superiority of the proposed method, achieving recognition accuracies of 97.1% and 98.3%, respectively, surpassing existing benchmark models. Moreover, the model demonstrates notable reductions in parameter complexity and computational cost, achieving an average inference time of only 12.6 ms, which confirms its strong real-time capability and suitability for embedded deployment. Overall, the proposed approach attains a desirable trade-off between accuracy and efficiency, offering meaningful implications for the advancement of lightweight biometric recognition systems.