In practical engineering applications, rotor-bearing systems inevitably exhibit inherent or residual faults such as imbalance and shaft-bow, originating from manufacturing tolerances, thermal deformation, or operational loading. Accurate monitoring of these faults and their evolution is fundamental to the effectiveness of modern prognostics and health management (PHM) frameworks. However, if such inherent faults are not identified at an early stage, substantial deviations in fault diagnosis may occur, thereby compromising the accuracy of subsequent prognostic assessments and maintenance strategies. This study presents a hybrid diagnostic methodology that integrates a physics-based model with neural network techniques to enhance rotor fault diagnosis. A Jeffcott rotor subjected to simultaneous disk imbalance and shaft-bow is used to demonstrate the methodology, and the results proves its superior capability for simultaneous fault identification. Nonetheless, discrepancies between model predictions and experimental results are observed, attributed to the presence of inherent faults within the rotor system. To address this issue, algorithms for inherent fault identification and compensation, supported by experimental verification, are developed. Following compensation, the accuracy in simultaneously diagnosing and estimating the parameters of imbalance and shaft-bow is significantly improved. The proposed methodology is designed for seamless integration into real-time monitoring systems of industrial rotating machinery.

The use of long-distance transoceanic cables equipped with high-loss loopbacks enables distributed sensing with a resolution determined by amplifier spacing, typically in the order of 50-100 km. Microwave-frequency fiber interferometry is a promising trans-mission technique for investigating long links supported by periodic optical amplification. In this paper, we propose a variant of this technique that ensures compatibility with links containing high-loss loopbacks, thereby transforming the integrated sensing approach into a distributed one. We highlight the critical modifications required to overcome challenges associated with the detection of multiple return signals, and we conduct a proof-of-principle experiment using a two-loop configuration. We demonstrate the concept by detecting and localizing low-frequency (<10 Hz) events-whether human-generated or induced by fiber stretchers-with span-level resolution. This validates the potential of the modified microwave-frequency interferometry approach for transoceanic cable monitoring in scenarios where high-loss loopbacks are present. We also present a theoretical analysis that evaluates the limits of the technique across different frequency ranges, in comparison with optical interferometry methods based on high-spectral-purity fiber lasers. The analysis shows that for long amplifier spacings (~100 km), micro-wave-frequency fiber interferometry exhibits a signal-to-noise ratio advantage at sub-Hz frequencies (<0.1 Hz) compared to state-of-the-art optical interferometers.

Maritime Domain Awareness (MDA) relies heavily on data acquired from high-resolution optical spaceborne sensors; however, processing this massive quantity of sensor data via traditional supervised deep learning is severely bottlenecked by its dependency on exhaustively annotated datasets. Under extreme data scarcity, conventional architectures suffer severe performance degradation, rendering them impractical for time-critical, zero-day deployments. To overcome this barrier, we propose a training-free inference paradigm that leverages the extensive pre-trained knowledge of Large Vision-Language Models (VLMs). Specifically, we introduce a Domain Knowledge-Enhanced In-Context Learning (DK-ICL) framework coupled with a Macro-Topological Chain-of-Thought (MT-CoT) strategy. This approach bridges the perspective gap between natural images and top-down optical sensor imagery by translating expert remote sensing heuristics into a strict, step-by-step reasoning pipeline. Extensive evaluations demonstrate the substantial efficacy of this framework. Armed with merely 4 visual exemplars per category as in-context triggers, our MT-CoT augmented VLMs outperform traditional models trained under identical scarcity by over 38% in F1-score. Crucially, real-world case studies confirm that this zero-gradient approach maintains robust generalization on unannotated, out-of-distribution coastal clutters, achieving performance parity with data-heavy networks trained on 50 times the data volume. By substituting massive human annotation and GPU optimization with scalable logical deduction, this paradigm establishes a resource-efficient foundation for next-generation intelligent maritime sensing networks.

Robust robotic autonomy remains challenging in complex environments, where loss of stability on uneven or slippery terrain can induce extreme accelerations and angular velocities. Such motions corrupt sensor measurements and degrade state estimation, motivating the need for improved algorithmic robustness. To investigate this issue, we introduce the Tumbling-Induced Gyroscope Saturation (TIGS) dataset, which consists of recordings from a mechanical lidar and an Inertial Measurement Unit (IMU) tumbling down a hill. The dataset contains angular speeds up to four times higher than those in similar datasets and is publicly available. We then propose two complementary methods to improve Simultaneous Localization And Mapping (SLAM) robustness and evaluate them on TIGS. First, Saturation-Aware Angular Velocity Estimation (SAAVE) estimates angular velocities when gyroscope measurements become saturated during aggressive motions, reducing angular speed estimation error by 83.4%. Second, Stretch-ICP, a novel registration and deskewing algorithm, enables reconstruction of smoother 6-Degrees Of Freedom (DOF) trajectories under aggressive motions compared to classical Iterative Closest Point (ICP). Stretch-ICP reduces linear and angular velocity errors by 95.2% and 94.8%, respectively, at scan boundaries. Together, these contributions improve the robustness and consistency of lidar-inertial state estimation under aggressive motions.

Tiny Machine Learning (TinyML) enables Machine Learning (ML) models to run on resource-constrained devices, which is critical for Industrial Internet of Things (IIoT) systems requiring low latency, energy efficiency, and local decision-making. Nevertheless, deploying TinyML in IIoT remains challenging due to diverse applications, hardware, frameworks, and deployment methodologies, highlighting the need for a structured and focused review. Existing review articles mainly address general IoT or edge AI, leaving a critical gap in a unified and systematic understanding of TinyML applications, system components, and methodologies within IIoT contexts. Consequently, this systematic literature review (SLR) addresses this gap by analyzing 35 peer-reviewed studies published between 2018 and 2026, offering a comprehensive and structured synthesis of TinyML-enabled IIoT systems. The selected works are synthesized across three major dimensions: applications, system components, and methodologies. In terms of applications, TinyML is primarily used for predictive maintenance, equipment monitoring, anomaly detection, energy management, and general-purpose applications. The general category captures cross-domain solutions that do not fit into a single industrial application. A comparative analysis of all application categories is conducted in terms of accuracy, latency, memory, and energy. For system components, a structured comparison shows how hardware, software, and sensing choices shape performance and applicability. Hardware platforms are grouped by microcontroller families, highlighting dominant types. Software frameworks are summarized, showing the widespread use of lightweight toolchains for on-device inference. Sensor types are categorized, with vibration sensing most common. They are supported by other sensing methods such as vision, sound (acoustic), and environmental sensors. Finally, the methodologies examined in this SLR provide a comprehensive view of the data foundations, model selection, and optimization strategies. In short, this SLR converges diverse TinyML-IIoT applications, microcontroller-based hardware, lightweight software frameworks, sensing modalities, varied datasets, and optimization strategies, while also identifying challenges and future research directions.

Fully decoupled radio access networks (FD-RANs) achieve spectral efficiency and coverage flexibility for 6G via independent uplink (UL) and downlink (DL) base station operation, yet dynamic user mobility brings critical challenges to joint user association and resource allocation. Asymmetric interference and heterogeneous base station capacities cause persistent network unfairness, while uncoordinated mobility management triggers ping-pong handovers and heavy handover overheads. To resolve these intertwined problems, we propose a fully decoupled, mobility-resilient and fairness-guaranteed framework, which integrates short-term congestion pricing with the long-term Jain fairness index for equitable resource distribution and introduces a composite handover penalty with a strict physical hysteresis margin to block invalid handovers. We formulate the optimization problem as a novel Sliding-Window Hysteresis-Integrated Fairness Two-Layer Multi-Armed Bandit (SHIFT-MAB) model, embedding an exponentially weighted moving average (EWMA) sliding-window mechanism to track real-time channel fluctuations efficiently. Theoretical analysis confirms the model's decoupling optimality, sublinear regret bound and fairness convergence. Extensive simulations show that SHIFT-MAB effectively suppresses invalid handovers, ensures high network fairness, optimizes system utility and achieves a superior handover-throughput trade-off.

In IIoT edge computing, multi-edge server collaborative scheduling faces two core issues due to random task arrivals, heterogeneous resources, and complex topology: traditional model-driven methods cannot make dynamic decisions in dynamic environments, and conventional MARL fails to characterize inter-node topological dependencies and load correlations. To address this, this paper investigates the joint optimization of task offloading, computing resource allocation, and SFC orchestration in IIoT, constructs a cloud-edge-end collaborative architecture, and models the problem as a POMDP to minimize the overall system cost under multiple constraints. A graph-guided value-decomposition MARL method is proposed, which extracts spatial topology and neighborhood-load features of edge nodes via a GNN and combines them with the QMIX framework to realize multi-agent centralized training and distributed execution. Simulations show that the algorithm converges stably under different server scales and task loads, significantly outperforms benchmark algorithms, and can suppress performance degradation in high-load scenarios, demonstrating its robustness and scalability in complex industrial environments.

Electrical Impedance Tomography (EIT) is a promising non-invasive sensing technique, yet its practical application in resource-constrained environments is often limited by the high computational cost of inverse image reconstruction. To address this challenge, we focus on specific sensing objectives rather than full image recovery. In this study, we propose a lightweight, task-oriented inference framework for object localization in EIT that bypasses the need to solve computationally expensive inverse reconstruction problems. This approach addresses the high computational demands and hardware complexity of conventional iterative methods, which often hinder real-time monitoring in resource-constrained edge computing environments. Training datasets were generated via finite element method (FEM) simulations for Opposite and Adjacent current injection configurations. A feedforward neural network was developed to independently estimate the radial and angular object positions as probability distributions. Our systematic evaluation revealed that the localization performance depends on the injection configuration and model depth; notably, the Opposite method achieved perfect classification accuracy (1.00) for radial estimation with an optimized architecture of four hidden layers, whereas the Adjacent method exhibited higher ambiguity. Results quantitatively evaluated using the Wasserstein distance show that the Opposite configuration produces more localized, unimodal probability distributions than the Adjacent configuration by utilizing current fields that traverse the entire domain. Compared with existing image-based reconstruction methods, including the conventional electrical impedance tomography and diffuse optical tomography reconstruction software (EIDORS ver.3.12), the proposed framework reduced energy consumption from 3.09 to 0.96 Wh, demonstrating an approximately 70% improvement in energy efficiency while maintaining a high localization accuracy without the need for iterative Jacobian updates. This task-oriented framework enables reliable, high-speed, and energy-efficient localization, making it well-suited for low-power EIT applications in mobile and embedded sensor systems.

The IEEE 802.15.6 standard defines wireless body area networks (WBANs) for communication in, on, and around the human body. However, commercially available hardware platforms that support direct experimental validation of IEEE 802.15.6-oriented WBAN studies remain limited. As a result, much WBAN research still relies on simulations or custom-built transceivers, leaving the practical validity of simulation results uncertain. In this study, we evaluated a configurable radio platform for GMSK-based narrowband WBAN PHY validation in the 420-450 MHz band by comparing theoretical calculations, ns-3 simulation results, and hardware measurements. Evaluations covered both on-body and off-body scenarios at transmit powers from -15 to -25 dBm. Our key findings are as follows: (1) lower transmit power consistently decreases the communication range in both simulated and hardware environments; (2) degradation trends in packet success rate are similar for both environments, supporting simulation credibility; and (3) in the off-body scenario, ns-3 simulations overestimate the communication range by approximately 10 m compared to hardware under identical conditions. The publicly available simulation framework facilitates reproducible WBAN research. Our results confirm that our ns-3 implementation can be used effectively to approximate key GMSK-based WBAN PHY behaviors in realistic conditions while identifying specific differences in range estimates.

This study investigates the frequency-dependent electrical conductivity of electrically conductive threads (also known as e-threads), particularly focusing on their inherently lower conductivity than traditional conductors like copper. While efforts have been made to electrically characterize conductive threads in the past, most studies have focused on DC or frequencies lower than 1 GHz. Recent works have evaluated attenuation up to 6 GHz, but they do not report bulk conductivity and lack validation in the context of antenna applications. In a major step forward, this study reports a systematic way of characterizing the surface conductivity of conductive yarns, for eight different thread types, from 10 MHz to 6 GHz. Different parameters such as insertion loss, attenuation, and conductivity are reported, determining the suitability of conductive yarns at specific frequencies. The study also reports the first frequency-dependent bulk conductivity of individual conductive threads. By measuring both surface and bulk conductivity, our work provides foundational data crucial for designing textile-based antennas and sensors. The practical relevance of the proposed approach is demonstrated through simulations and measurements of a broadband log-spiral antenna and a single-turn loop antenna. Overall, this research contributes valuable insights into the integration of e-textiles in smart fabric applications, paving the way for further innovations in this evolving field.