Search Results (864)

Self-powered monitoring systems capable of scavenging ambient mechanical energy are a highly desirable solution to eliminate the reliance on batteries and grid power in remote and distributed atmospheric sensing networks. However, the widespread adoption of such systems is severely hindered by the insufficient output power density of current energy harvesters, which struggle to simultaneously drive environmental sensors, data acquisition units, and wireless transmission modules. In this work, we report a highly integrated hybrid power generation system that couples a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) to efficiently harvest low-frequency mechanical energy from the surroundings. Through systematic structural optimization and synergistic matching of the two transduction mechanisms, the device achieves an outstanding volumetric power density of 129.9 W·m⁻³, which represents one of the highest values ever reported for hybrid nanogenerators targeting self-powered environmental applications. The output characteristics of both the TENG and EMG units under varying load impedances are thoroughly characterized, revealing the optimal operating points for maximum power extraction. A tailored power management module, consisting of rectification, energy storage, and regulation circuits, is designed to convert the irregular alternating output into a stable direct-current supply. To demonstrate the practical viability of the system, we construct a complete self-powered atmospheric environment monitoring node, which integrates multiple environmental sensors, a data acquisition module, and a wireless transmission module. Driven exclusively by the hybrid TENG–EMG generator under ambient mechanical excitation, the node successfully performs real-time sensing, signal processing, and remote data communication without any external power input. This work not only provides a record-high power density among hybrid generators for environmental monitoring, but also establishes a feasible pathway toward maintenance-free, widely distributed, and truly autonomous atmospheric sensing networks. The presented strategy of maximizing volumetric power density through hybrid design and impedance engineering can be readily extended to other self-powered systems. Full article

This work aims to study and improve the long-term stability of conductimetric biosensors for urea detection in clinical and environmental samples, which are fundamentally limited by complex thermal and temporal drifts due to temperature-sensitive enzyme kinetics, variations in ionic mobility, and the progressive degradation of the sensing layer. The biosensor targets the urea concentration range 0.01–30 mM, validated against experimental data and covering the clinically relevant range for blood urea detection (2.5–7.5 mM), urine (20–40 mM), and environmental monitoring applications. Conventional calibration techniques, such as the conventional calibration method (based on reference measurements), and purely deterministic correction methods, such as deterministic methods (based on known fixed equations), often prove insufficient because they struggle to capture the non-stationary and inherently stochastic nature of these drifts. In this work, we propose an original hybrid probabilistic framework that synergistically combines machine learning and Bayesian inference for robust adaptive drift compensation. A Random Forest model is first implemented to model the deterministic nonlinear relationships between environmental parameters (temperature, pH, CO₂ concentration) and the sensor response. The residual temporal drift is then explicitly modeled as a non-stationary latent stochastic process using Bayesian inference based on a Gaussian process. This approach allows continuous online model updating, real-time uncertainty quantification, and automatic detection of anomalies. The models were trained and validated on a large dataset obtained from multiphysics simulations carried out in COMSOL Multiphysics 5.6. These simulations incorporated enzymatic reactions, thermal effects, and chemical dynamics taking place inside the sensor. Experimental results show that the hybrid approach substantially enhances sensor performance, lowering the root mean square error (RMSE) to below 0.8 μS/cm (corresponding to less than 0.5% of the full-scale response) over a wide temperature range (15–45 °C) and across extended operating periods. This represents a clear improvement over conventional compensation method. By merging the predictive power of ensemble learning with a probabilistic Bayesian model of dynamic drift, this study introduces a fresh perspective on the design of intelligent, self-adaptive, and drift-resistant conductimetric biosensors. The proposed framework holds strong potential for reliable, long-term autonomous operation in urea reliable, long-term autonomous operation in urea monitoring across biomedical diagnostics (kidney/liver function assessment) and environmental surveillance (water eutrophication prevention). Full article

This paper presents a distributed coordination framework for simultaneous multi-target tracking using a mobile wireless sensor network (MWSN) based on discrete-event-system principles. The proposed framework employs a finite-state-machine architecture, where autonomous mobile sensors sequentially process detection and tracking events. Unlike passive tracking approaches that react to target loss after it occurs, the proposed strategy implements predictive handover through Extended-Kalman-Filter-based uncertainty propagation. This enables sensors to anticipate target loss and to reposition auxiliary sensors in advance, acquiring targets along their predicted trajectories. A bidding-based allocation mechanism coordinates sensor assignments by evaluating four competing objectives: network preservation, spatial proximity to handover points, temporal mission feasibility, and estimation uncertainty. The proposed framework integrates four components: EKF-convergence-triggered proactive handover, multi-objective competitive bidding, distributed min–max conflict resolution, and fusion-driven proportional navigation. Unlike existing methods, auxiliary sensors navigate using confidence-weighted EKF estimates shared by neighboring sensors rather than their own measurements. An ablation study over ten Monte Carlo trials confirms that each component contributes independently, with EKF-based predictive triggering identified as the dominant performance driver. Full article

Industrial systems increasingly rely on MQTT-based message streaming to enable automated, data-driven production processes at the network edge. While semantic models such as the SSN/SOSA ontology enable machine-interpretable descriptions of observations and actuations, an explicit model of message transport is rarely considered. Consequently, MQTT-based communication remains opaque, particularly regarding information processing, hindering the semantic analysis of application-specific topic structures and the behavior of transport protocols. To close this gap, this work introduces the revised MQTT4SSN ontology as a key contribution, extending existing semantic models with protocol-aware representations of MQTT entities, control packets, and transport-level interactions. MQTT4SSN enables end-to-end semantic traceability, from sensor observations and actuator controls to the underlying message transmission within distributed systems. Building on this contribution, the MQTT2RDF integration framework incorporates MQTT4SSN as its core to capture live MQTT traffic and represent both payload meaning and transport-level provenance within an RDF knowledge graph. This work presents a novel approach for representing edge computing and information processing over MQTT, addressing two key challenges. First, the framework supports semantic interpretation of topic hierarchies and provides configurable mappings between MQTT topics, payload structures, and observation or actuation semantics. This approach facilitates the setup of edge computing systems and enables context-aware subscription management and structured data formatting, thereby improving interoperability between heterogeneous deployments. Second, transport-level provenance analytics provide a semantic basis for query-based detection, classification support, and diagnostic analysis of malformed or incomplete MQTT communication. The approach provides explainable, traceable information processing through transport provenance, which is essential for safety-critical industrial environments. The contributions are validated through an industrial use case from a production environment, demonstrating its applicability for system monitoring, troubleshooting, and semantic analytics of MQTT-based infrastructures. Full article

Incomplete spatio-temporal (ST) data from sensor networks in precision agriculture often limits environmental modeling and decision-making accuracy. To address this, we propose the Spatio-Temporal Conditional Diffusion Framework (ST-CDF), a generative approach for high-fidelity data reconstruction. The framework’s core is a deep denoising network that integrates a Graph Attention Network (GAT) to explicitly model non-Euclidean spatial correlations, a Differential Attention Transformer to capture abrupt temporal dynamics, and an Inverse Discrete Wavelet Transform (IDWT) module to preserve multi-scale signal details. The generative process is constrained by a physics-informed training objective, which injects known physical laws (i.e., the Penman–Monteith equation for reference evapotranspiration, $E{T}_0$) as an inductive bias, ensuring the imputed data maintains physical consistency. For privacy-preserving deployment on resource-constrained IoT devices, we extend the framework with a Federated Cluster-Guided Distillation (Fed-CGD) strategy. We conducted extensive experiments against established methods on two real-world agricultural datasets. ST-CDF demonstrated improved imputation accuracy across evaluated metrics. Its efficacy was most pronounced in the physically-demanding $E{T}_0$ calculation task, where data imputed by ST-CDF at an 80% missing rate achieved a Root Mean Square Error (RMSE) of 0.3485 and a Coefficient of Determination ($R^2$) of 0.7558, outperforming the baseline models. Furthermore, we explore ST-CDF as an explainable (XAI) framework for active agricultural decision support, demonstrating its utility in performing counterfactual simulations of “what-if” interventions, such as irrigation. The findings highlight ST-CDF as an effective, physically-grounded, and interpretable tool for data-driven scientific computation and precision agriculture. Full article

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a $+27.36\%$ reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to $15.2 \mathrm{s}$, and restricting transient overshoot to just $0.18\%$. Full article

This article introduces the current status of the 0.18-micron CMOS MEMS foundry service platform provided by the Taiwan Semiconductor Research Institute (TSRI), extensively covering the CMOS MEMS components that it has supported in development and fabrication. It also attempts to expand the foundry service scope to the broader categories of capacitive sensors and electrostatic actuators. On the one hand, for fabless MEMS component designers, TSRI currently directly allows the design of two types of components: flow sensors with uniformly perforated membranes and actuators with comb-shaped interdigital electrodes. This service also includes tape-out and wire bonding packaging procedures, following procedures similar to those used by general IC designers. On the other hand, this article specifically presents a clear and feasible approach for MEMS designers equipped with simple wet-etching facilities and a clear and feasible approach to develop further CMOS MEMS components such as capacitive pressure sensors, accelerometers, micro mirrors, and scratch drive actuators with minimal post-processing and chip packaging steps. This work provides a practical CMOS-MEMS design and post-processing guideline for extending the current TSRI foundry platform toward capacitive sensing and electrostatic actuation applications with minimal additional fabrication complexity. Full article

Direction-of-arrival (DOA) estimation of cyclostationary signals is an important problem in array signal processing, especially in sensor-limited and underdetermined scenarios. Sparse arrays and cyclostationary statistics can improve virtual degrees of freedom and target selectivity, but incomplete difference coarray information caused by missing lags may degrade virtual covariance reconstruction and reduce the reliability of DOA estimation in closely spaced, coherent, and interference-contaminated environments. To address this issue, this paper proposes a cyclostationary DOA estimation method based on an augmented extended coprime array (AECA), SVT-based hole recovery, and weighted atomic norm minimization (ANM). The proposed method first constructs the cyclic correlation matrix at the target cyclic frequency and maps it into the AECA-based virtual coarray domain. Redundant lag observations are then aggregated, and an iterative hole recovery procedure is applied to obtain an initial structured virtual covariance matrix. On this basis, a weighted ANM-based covariance refinement model is introduced, where directly observed lags and SVT-recovered hole entries are assigned different confidence levels. The final DOA estimates are obtained using MUSIC on the refined virtual covariance matrix. Simulation results under the considered underdetermined, closely spaced, coherent-source, and interference-contaminated scenarios show that the proposed method achieves lower RMSE and clearer spectral responses than the selected baseline methods. Additional ablation, parameter sensitivity, cyclic frequency mismatch, non-Gaussian noise, and runtime analyses further clarify the contribution, robustness range, and computational cost of the proposed framework. Full article

The rapid development of Internet of Things technologies has accelerated the deployment of smart home systems. However, perception solutions based on visual sensors remain constrained by illumination sensitivity, occlusion, and privacy concerns. Frequency-modulated continuous-wave (FMCW) millimeter-wave radar provides a promising alternative because it operates independently of lighting conditions, is robust to environmental changes, and preserves user privacy. To address multiple-extended-target tracking in cluttered indoor environments, this paper proposes a high-accuracy tracking algorithm that combines an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, an optimized Nearest-Neighbor Data Association (NNDA) scheme, and an Extended Kalman Filter (EKF). The improved DBSCAN algorithm introduces spatial-extent constraints, velocity-consistency checks, and candidate-cluster validation to cluster raw radar point clouds and convert extended targets into representative point targets with little additional computational cost. The optimized NNDA scheme then integrates clustering information into the association process, improving the matching accuracy between existing tracks and current measurements. Finally, the EKF estimates the state of each target from the associated measurements. Real-world experiments show that the proposed algorithm achieves tracking errors below 0.4 m in typical motion scenarios, maintains continuous tracking in two-person crossing scenarios, and reaches 93.3% counting accuracy in five-person scenarios. These results outperform the tracking system based on the commercial Texas Instruments (TI) IWR6843ISK millimeter-wave radar evaluation board. The proposed method offers a reliable and privacy-preserving sensing solution for smart homes, elderly care, and intelligent building applications. Full article

When operating in complex and obstacle-dense environments, micro UAV swarms often face severe cooperative positioning failures due to transient non-line-of-sight (NLOS) interference and cascaded inertial sensor drift. To address this, this work proposes a fault-tolerant positioning framework integrating multi-agent deep reinforcement learning with cooperative extended Kalman filtering (MADRL-CEKF). The system incorporates a link-level dynamic soft isolation mechanism that dynamically adjusts observation covariance to effectively sever paths of cooperative error contagion. An adaptive Markov smoothing constraint is mathematically embedded to mitigate high-frequency control jitter typical of AI-driven policies. Crucially, the framework implements a resource-aware multi-objective reward architecture tailored for micro UAVs. Evaluated through high-fidelity simulations and offline physical datasets, the proposed framework achieves a 96.01% reduction in average tracking error (RMSE) under extreme multi-node cascaded failures, completely preventing system divergence. Furthermore, through autonomous multi-objective trade-offs, the system reduces processing delay by 44% (to 25.1 ms) and computational energy consumption by 41% with only a marginal accuracy compromise of 0.16 m, strictly keeping the execution time within the 50 ms real-time threshold. The MADRL-CEKF framework effectively bridges the gap between sophisticated AI decision-making and strict engineering constraints, providing a highly robust and resource-efficient navigation paradigm for swarm robotics. Full article

Indispensable roles in personalized health monitoring and human–machine interaction are played by flexible humidity sensors. However, high costs and complex vacuum processes are often involved in current fabrication methods, thereby restricting their broader applications. In this work, a high-performance flexible capacitive humidity sensor is presented, wherein a ternary composite of graphene oxide, PEDOT:PSS, and MXene (GO-PEDOT:PSS-MXene) is loaded onto a laser-induced graphene (LIG) interdigitated electrode. A pronounced synergistic effect among the three components is systematically exploited by this multidimensional architecture to significantly optimize the overall sensing performance. Within a relative humidity range extending from 11% to 97%, a remarkable measurement sensitivity of 18,643.02 μF/%RH is recorded. Furthermore, a characteristic negative capacitive response is consistently induced by moisture-driven microstructural swelling, by which the internal interlayer spacing is increased. The continuous monitoring of human respiratory rhythms and precise non-contact spatial sensing is successfully enabled by rapid response and recovery times of 31.7 s and 11.2 s, respectively. Uniquely, a vacuum-free, synergistic multidimensional architecture is successfully utilized to achieve an ultrahigh sensitivity. Practically, a highly scalable and low-cost paradigm is established by this research for the mass deployment of future wearable electronic systems across diverse monitoring scenarios. Full article

Maritime piracy poses an ongoing operational threat to commercial shipping in high-risk regions, where fast-approach attack scenarios leave vessel crews with critically limited reaction time. Automated threat detection technologies—including radar, electro-optical, and thermal imaging sensors—are increasingly integrated into maritime security architectures; however, their operational effectiveness has rarely been evaluated through quantitative engineering frameworks. This study presents a technical analysis of mechatronic detection systems, focusing on detection range, reaction time constraints, and classification reliability under representative piracy conditions. A kinematic time-to-contact model is introduced to quantify how detection distance directly governs the available defensive response window: extending reliable detection from 1 NM to 3 NM expands the reaction margin from approximately 171 s to over 440 s, a difference that may determine whether protective measures can be executed in time. Classification performance is assessed using standard metrics, with recall identified as the operationally critical indicator in asymmetric threat environments. Model-based simulations indicate that, under the assumed scenario parameters, automated detection systems can reduce operational risk by up to 45%, illustrating the sensitivity of survivability outcomes to early detection capability. The findings translate directly into design thresholds for sensor range, algorithmic sensitivity, and processing latency, providing actionable engineering recommendations for practitioners responsible for maritime security system design and vessel protection planning. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

Accurate path loss prediction is essential for reliable and energy-efficient operation of dense Wireless Sensor Network–Internet of Things (WSN–IoT) systems, where radio transmission dominates node energy consumption and significantly impacts network lifetime. However, existing empirical or simulated models cannot achieve high prediction accuracy without explicitly linking statistical error metrics to system-level design parameters, thus limiting their practical interpretability in deployment scenarios. This work presents an extensive comparative evaluation among well-known propagation models versus machine learning regressors, and a lightweight convolutional neural network (CNN) for path loss prediction, using transmitter–receiver distance and carrier frequency as input features. A pairwise communication model is adopted to ensure consistent analysis across heterogeneous environments while preserving physical interpretability of the propagation process. Building upon this evaluation, a unified analytical framework is proposed that correlates path loss (PL) prediction accuracy to system-level metrics relevant to WSN–IoT design. Moreover, in this work we apply the Root Mean Square Error (RMSE) of the best-performing model as an empirical estimate of the shadowing standard deviation, under standard statistical assumptions, thereby allowing its direct use in link budget and fade margin calculations. Extensive experimental results across five heterogeneous wireless link datasets demonstrate that improved prediction accuracy leads to reduced transmission power requirements, lower energy consumption, enhanced communication reliability, and extended node lifetime. Full article

Soft pneumatic grippers are well-suited for grasping irregular objects owing to their inherent compliance and ability to adapt to a wide range of shapes and sizes. However, their ability to quantitatively estimate object size during the grasping process remains limited. To address this limitation, this study proposes a soft pneumatic gripper integrated with a piezoresistive CNT/PDMS composite sensor and investigates the feasibility of object size estimation using only sensor signals. The pressure-sensing characteristics of the CNT/PDMS sensor were evaluated over a pressure range of 0–500 kPa, and the 1 wt% CNT/PDMS sensor exhibited the highest sensitivity of approximately 0.016 kPa⁻¹ in the initial linear pressure region. To this end, the normalized resistance response under applied pneumatic pressure was analyzed independent of external visual information, and a size estimation method was established based on the relationship between initial contact pressure and object diameter. Grasping experiments using spherical objects of varying diameters revealed that the resistance response patterns were clearly distinguishable according to object size, with larger objects exhibiting significant resistance changes at lower applied pressures. These findings demonstrate the feasibility of estimating the size of a grasped object based on the grasp onset pressure derived from the sensor response. The results of this study provide a foundation for future soft robotic systems capable of recognizing contact conditions and object size through sensor-based feedback. Furthermore, these findings may be extended to adaptive manipulation technologies involving real-time pneumatic pressure control. Full article