Search Results (7,167)

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI’s role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI–SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow’s intelligent sensing infrastructure. Full article

Underwater images often suffer from color distortion and low contrast, severely limiting the reliability of visual perception systems. Existing methods struggle to balance enhancement quality and computational efficiency. To address this issue, we propose PCF-Net (Perception-driven Color Fusion Network), a lightweight dual-branch network for underwater image enhancement based on a stable HSV-CS (Hue-Saturation-Value with sine–cosine transformation) color-space representation. Specifically, a sine–cosine transformation is introduced to construct a stable HSV-CS color space, effectively avoiding hue discontinuities at boundary regions in conventional HSV representations. To compensate for underwater degradation, a Color-Bias-Aware module and a Value-Confidence module are designed to adaptively correct color distortion and luminance degradation. Furthermore, a lightweight Channel-Spatial Adaptive Gated Fusion module dynamically aggregates features from the RGB and HSV-CS branches in a perception-driven manner. The overall architecture incorporates multi-branch re-parameterizable convolutions, significantly reducing computational cost while preserving strong representational capacity. Extensive experiments on underwater image enhancement benchmarks, including UIEB and RUIE, demonstrate that PCF-Net achieves state-of-the-art performance in terms of PSNR, SSIM, and UIQM, along with visually superior color correction and contrast enhancement. With only 0.17 M parameters, the proposed model runs at 118.6 FPS on an RTX 3090 and 35.3 FPS on a Jetson Orin Nano at a resolution of 512 × 512, making it well suited for resource-constrained real-time underwater vision applications. Full article

The navigation of ships in complex shallow water areas is constrained by various factors such as water depth, channel boundaries, and environmental interference. Therefore, it is crucial to improve the adaptability and effectiveness of collision avoidance decisions for ships in complex shallow water scenarios. To address these issues, this paper proposes a multi-factor constrained autonomous decision-making method for complex shallow water vessel maneuvering. Firstly, a digital transportation environment was constructed by combining dynamic and static information, such as water depth, tides, channel boundaries, changes in maneuvering characteristics, and navigation rules, and a navigable water area model that was suitable for shallow water was proposed. Then, considering the constraints of ship maneuverability and the navigation environment, a shallow water ship motion model affected by wind flow was developed. A complex shallow water adaptive maneuvering coupled decision-making method was constructed, considering the influence of ship navigation rules and channel constraints. This method utilizes the Kalman filtering algorithm to correct residuals and predict the maneuvering of the target vessel. Integrated improved heading control and guidance algorithms achieved automatic heading control and future position prediction. Through testing and verification in the complex waters of the Yangtze River estuary, the results show that the autonomous collision avoidance decision-making method proposed in this paper can effectively make collision avoidance decisions in complex multi-ship shallow water areas. This study can provide innovative and practical solutions for the technological development of autonomous ship collision avoidance decision-making. Full article

Wearable biosensors increasingly stream multi-channel physiological and behavioral data outside the laboratory, yet most deployments still end in dashboards or threshold alarms that leave interpretation open to the user. In high-stakes domains, such as military, emergency response, aviation, industry, and elite sport, the constraint is rarely data availability but the cognitive effort required to convert noisy signals into timely, actionable decisions. We argue for on-person cognitive co-pilots: systems that integrate multimodal sensing, compute probabilistic state estimates on devices, synthesize those states with task and environmental context using locally hosted large language models (LLMs), and deliver recommendations through attention-appropriate cues that preserve autonomy. Enabling conditions include mature wearable sensing, edge artificial intelligence (AI) accelerators, tiny machine learning (TinyML) pipelines, privacy-preserving learning, and open-weight LLMs capable of local deployment with retrieval and guardrails. However, critical research gaps remain across layers: sensor validity under real-world conditions, uncertainty calibration and fusion under distribution shift, verification of LLM-mediated reasoning, interaction design that avoids alarm fatigue and automation bias, and governance models that protect privacy and consent in constrained settings. We propose a layered technical framework and research agenda grounded in cognitive engineering and human–automation interaction. Our core claim is that local, uncertainty-aware reasoning is an architectural prerequisite for trustworthy, low-latency augmentation in isolated, confined, and extreme environments. Full article

This study examines whether China’s Intelligent Manufacturing Demonstration Projects (IMDPs, 2015–2018) can improve firms’ environmental, social, and governance (ESG) performance and thereby strengthen the quality of green transformation in manufacturing. Exploiting the staggered rollout of IMDPs as a quasi-natural experiment, we combine propensity score matching with a multi-period difference-in-differences design using panel data on Chinese listed manufacturing firms from 2009 to 2022. We find that IMDP participation increases ESG ratings by approximately 0.14 rating levels relative to comparable non-participating firms. Mechanism analyses suggest that the effect operates through higher innovation efficiency and improved cost management, consistent with a channel of capability upgrading and resource reallocation toward sustainability-related activities. The effect is stronger for firms under intense competitive pressure, at the growth stage, and in capital-scarce industries, indicating that industrial policy can be particularly valuable where market frictions constrain green investment. Importantly, we go beyond ESG scores by constructing measures of greenwashing and ESG rating uncertainty, and show that IMDPs reduce greenwashing and lower ESG uncertainty. These results imply that intelligent manufacturing policies can generate economically meaningful benefits by improving firms’ sustainability performance and the credibility of ESG information, which are central to capital allocation and the effectiveness of green governance. Full article

This study proposes a feature-enhanced diffusion model based on wavelet transform and Mamba to address the issues of low audio realism, inadequate text relevance, and slow inference speed in text-guided sound effect generation. A wavelet transform-based downsampling module is designed to mitigate the loss of high-frequency feature information during the downsampling process of the diffusion model, thereby enhancing the realism of the generated audio. A multi-scale feature extraction and fusion method is employed to capture both local and global acoustic information, while the channel attention mechanism further strengthens the model’s focus on text-relevant key features. Additionally, an optimization method based on Mamba and adaptive weight adjustment is proposed, which takes advantage of Mamba’s efficient information processing mechanism and learnable parameters to optimize skip connections, improving model training and inference efficiency without adding substantial computational cost. Experiments show that the model achieves FAD and KL scores of 1.608 and 1.609, respectively, reflecting improvements of 33.8% and 26.1% compared to the baseline model. Full article

Background/Objectives: Nurses’ turnover intentions are strong predictors of actual turnover, which increases costs, reduces care quality, and destabilies the workforce. This study aimed to identify the key predictors of nurses’ turnover intentions using advanced machine learning methods and to explore how demographic, well-being, and work environment factors contribute to these intentions. Methods: Cross-sectional data were collected from 2459 nurses employed across various healthcare institutions. We used multichannel invitation and snowball sampling. An artificial neural network regression model was applied, combined with iterative feature selection and SHAP analysis, to identify the most important predictors of turnover intentions and to examine nonlinear and context-dependent relationships among variables. Results: Seven predictors explained 49.8% of the variance in turnover intentions, outperforming traditional linear models. Age was the strongest predictor, with younger nurses demonstrating a substantially higher likelihood of intending to leave; this association was nonlinear, with intentions decreasing more sharply at older ages. Job satisfaction and burnout were also strong predictors, particularly among younger nurses. Four work environment factors further contributed to turnover intentions: managerial support functioned as a protective factor, interpersonal conflict increased intentions to leave, limited professional development opportunities were strongly associated with higher turnover intentions, and role conflict showed heterogeneous effects. Conclusions: Machine learning approaches enhance understanding of complex workforce dynamics and enable more precise identification of high-risk groups. The findings support age-sensitive retention strategies, proactive monitoring of nurse well-being, and organizational interventions to strengthen managerial support and professional development, ensuring workforce stability and sustainable healthcare service delivery. Full article

The Medical Internet of Things (MIoT) has promoted smart healthcare through the deep integration of wearable devices, wireless communication, and cloud services. However, this framework faces security risks, as attackers may exploit public channels to impersonate legitimate devices or services and steal sensitive data. Therefore, establishing authentication between wearable devices and servers prior to data transmission is crucial. Existing schemes suffer from two critical drawbacks: vulnerability to quantum attacks and excessively high communication overhead, highlighting the need for improved solutions. The authors of this paper present a multi-factor identity authentication protocol to achieve post-quantum security and privacy protection. The scheme integrates lattice-based Kyber key encapsulation and a fuzzy commitment mechanism to secure biological templates and enable post-quantum key agreement. Additionally, hash functions and lightweight error correction codes are employed to reduce terminal communication overhead. The security of the scheme is rigorously proved in the Real-or-Random model, and the analysis confirms that the scheme satisfies common security requirements for wireless networks. The proposed scheme is also compared with existing schemes, and the results demonstrate that it achieves a balance between security and overhead. Full article

The manipulation of vector beams (VBs) with longitudinally variant polarization states is an important research topic and has potential applications in classical and quantum fields. In this study, we propose a half-wave plate dielectric metasurface composed of two interleaved sub-metasurfaces to generate longitudinally separated dual-channel vectorial structured light fields. The propagation and Pancharatnam–Berry phases are employed to construct hyperbolic, helical, and opposite gradient phases for focusing wavefronts, generating circularly polarized (CP) vortices, and deflecting CP vortices with the same chirality in opposite directions. Consequently, dual-channel higher-order or hybrid-order Poincaré (HOP or HyOP) beams are generated along the optical axis under elliptically polarized illumination, and their polarization states evolve along an arbitrary pair of antipodal meridians on the HOP or HyOP sphere by varying the ellipticity of the incident light, the propagation-phase topological charge, and the rotation order of the meta-atom. The consistency between the theoretical and simulated results demonstrates the feasibility and practicability of the proposed method. This study is significant for compact, integrated, and multifunctional optical devices, and provides an innovative strategy to extend optical field manipulation from two-dimensional to three-dimensional space. Full article

Photonic quantum communication enables secure and high-fidelity information transfer beyond classical limits, with direct relevance to emerging quantum networks operating in free-space environments. While physical-layer models of depolarizing noise, Gamma–Gamma turbulence statistics, entanglement swapping, and decoy-state QKD security bounds are individually well established, prior work typically treats these components in isolation or under fixed network assumptions. In this work, we develop a unified topology-aware analytical framework that simultaneously integrates free-space optical link budgets, turbulence-induced visibility degradation, depolarizing qubit noise, multi-hop entanglement cascade dynamics, teleportation fidelity thresholds, CHSH nonlocality certification, and asymptotic decoy-state secret key rate bounds across star, mesh, and ring graph structures. Rather than introducing new physical channel models, we demonstrate that identical physical links exhibit fundamentally different end-to-end performance once embedded within different network topologies. Mesh architectures minimize visibility cascade through hop-count reduction but incur quadratic hardware scaling. Star topologies minimize link count but concentrate noise and synchronization overhead at the hub. Ring configurations offer linear hardware scaling with multiplicative fidelity degradation. The results establish topology as a first-order design parameter in near-term free-space quantum networks operating without full quantum repeater infrastructures. While motivated by distributed multi-agent architectures, the framework applies broadly to terrestrial, airborne, and satellite-assisted photonic quantum communication systems. Full article

Maritime small object detection is critical for UAV-based sea surveillance but remains challenging due to the small size of targets and interference from sea reflections and waves. This paper proposes SeaLSOD-YOLO, a lightweight detection algorithm based on YOLOv11, designed to improve small object detection accuracy while maintaining real-time performance. The method incorporates four key modules: Shallow Multi-scale Output Reconstruction, which fuses shallow and mid-level features to preserve fine-grained details; SPPF-FD, which combines spatial pyramid pooling with frequency-domain adaptive convolution to enhance sensitivity to high-frequency textures and suppress sea-surface interference; attention-based feature fusion, which emphasizes small object features through channel and spatial attention; and dynamic multi-scale sampling, which optimizes feature representation across different scales. Experiments on the SeaDroneSee dataset demonstrate that, compared with YOLOv11s, the proposed method improves precision from 75.6% to 81.9%, recall from 62.6% to 73.5%, and mAP@0.5 from 67.9% to 77.0%. The mAP@0.5:0.95 also increases from 41.1% to 44.9%. The model achieves an inference speed of 256 FPS. Although the parameter size increases from 18.2 MB to 30.8 MB, the method maintains a favorable balance between detection accuracy and computational efficiency. Comparative evaluation further shows superior performance in detecting small maritime objects such as buoys and lifeboats. These results indicate that SeaLSOD-YOLO effectively balances accuracy, efficiency, and real-time capability in complex maritime environments. Future work will focus on further optimization of attention mechanisms and upsampling strategies to enhance the detection of extremely small targets. Full article

Advanced mobile communication standards of the fifth and subsequent generations widely use beamforming technology. While many publications on this topic rely on simulation tools, some work has been dedicated to experimental testing using software-defined radio (SDR) platforms. These platforms are often expensive and require significant expertise to configure. This paper proposes a novel cost-effective method for combining a pair of dual-channel Universal Software Radio Peripheral (USRP) B210 boards into a four-element antenna array direction of arrival estimation testbed using Metronom synchronization devices. The hardware and developed software implementation is detailed, including the antenna layout and software modules, based on USRP Hardware Driver, that provide the frequency and time synchronization necessary for amplitude-phase processing. Experimental validation of the testbed using the MUltiple SIgnal Classification (MUSIC) algorithm demonstrates high stability of angle of arrival estimates, with a standard deviation not exceeding 0.4°. The algorithm achieved a resolution of 16.1° for two sources, which surpasses the half-power beamwidth of 25.6°. The theoretical significance of this work lies in the scientific validation of combining SDR devices with the precise synchronization required for beamforming. Its practical value is in enabling the experimental testing of beamforming without the need for costly multichannel SDR hardware. Full article

Automated identification of lumbar vertebrae plays a key role in modern spine analysis, offering valuable assistance for diagnostic assessment and preoperative decision-making. Despite recent progress in deep learning-based detection methods, accurately localizing vertebral structures remains challenging due to anatomical variability and heterogeneous image quality. To address the difficulty of capturing subtle vertebral structures, we introduce a Multi-Scale Channel Attention Block (MSCABlock) integrated into the YOLO11 backbone. Unlike conventional attention-based or multi-scale convolutional designs, MSCABlock jointly exploits channel-wise feature interaction and multi-scale receptive fields to enhance both local detail sensitivity and contextual representation, while preserving computational efficiency. The proposed approach is designed to improve detection performance without significantly increasing model complexity. Our model is trained and validated using only the AP-view images from the Burapha University Lumbar-Spine Dataset (BUU-LSPINE), which provides well-annotated lumbar spine X-ray images from 400 unique patients. The proposed approach operates in a fully end-to-end manner, allowing vertebrae to be identified directly from input images without relying on handcrafted feature engineering or complex preprocessing pipelines. Experimental evaluations show that the proposed model achieves strong detection performance, with mAP@0.5 and mAP@0.5–0.95 reaching 0.982 and 0.79, respectively, alongside a precision of 0.93 and a recall of 0.975. Compared with the YOLO11 baseline, ablation and efficiency analyses demonstrate that MSCABlock consistently improves detection performance. It introduces only marginal increases in model parameters and computational cost, thereby preserving a lightweight architecture and maintaining efficient inference. These results show that the optimized YOLO11-based system generalizes well across lumbar levels. It maintains reliable detection under challenging conditions, providing robust automated localization to support large-scale clinical spine analysis. Full article

Semantic segmentation of remote sensing images (RSIs) is a fundamental task in geoscience research. However, designing efficient feature fusion modules remains challenging for existing dual-branch or multi-branch architectures. Furthermore, existing deep learning-based architectures predominantly concentrate on spatial feature modeling and context capturing while inherently neglecting the exploration and utilization of critical frequency-domain features, which is crucial for addressing issues of semantic confusion and blurred boundaries in complex remote sensing scenes. To address the challenges of feature fusion and the lack of frequency-domain information, we propose a novel dual-path feature extraction network (DFENet) in this paper. Specifically, a dual-path module (DPM) is developed in DFENet to extract global and local features, respectively. In the global path, after applying the channel splitting strategy, four feature extraction strategies are innovatively integrated to extract global features from different granularities. According to the strategy of supplementing frequency-domain information, a frequency-domain feature extraction block (FFEB) dominated by discrete Wavelet transform (DWT) is designed to effectively captures both high- and low-frequency components. Experimental results show that our method outperforms existing state-of-the-art methods in terms of segmentation performance, achieving a mean intersection over union (mIoU) of 83.09% on the ISPRS Vaihingen dataset and 86.05% on the ISPRS Potsdam dataset. Full article

Heterogeneous multi-core systems (HMCSs) typically face a dilemma: heuristics (e.g., Linux CFS) are fast but blind to global constraints, while meta-heuristics (e.g., GAs) are globally optimal but too slow for real-time OS interaction. To bridge this gap without relying on “black-box” neural networks, we introduce the Tissue P System-Inspired Task Allocator (TPSTA). By mapping HMCS and parallel task scheduling to Tissue P System models and vectorized linear algebra problems, TPSTA achieves a computational complexity of $\mathrm{O}\left(\mathrm{M}/\mathrm{W}\right)$, effectively compressing the decision space. Our rigorous evaluation across four dimensions reveals a system strictly bound by physical constraints rather than algorithmic heuristics. (1) Under sufficient resource provisioning (four chips), TPSTA achieves a 0.00% Deadline Miss Ratio (DMR). Crucially, stress tests on constrained hardware (two chips) show graceful degradation to a 12.88% DMR, matching the optimal theoretical bound of EDF, whereas standard heuristics collapse to failure rates > 68%. On a massive 4096-core cluster, TPSTA outperforms the Linux GTS scalar baseline by 14.4×, maintaining low latency where traditional algorithms fail (>8 s). (3) Adaptability: The system demonstrates adaptive routing in handling hardware heterogeneity; without explicit rule-coding, it autonomously prioritizes data locality during NUMA transfers and migrates compute-bound tasks during thermal throttling events. (4) Physical Limits: Finally, our roofline analysis confirms that while the algorithmic speedup is theoretically linear, practical performance saturates at ~375× due to the Memory Wall, validating the isomorphism between synaptic bandwidth and hardware memory channels. Full article