Search Results (3,573)

In complex marine environments, achieving low-cost, highly reliable, and continuous navigation is crucial for the intelligent and autonomous operation of unmanned surface vehicles (USVs). Currently, the integrated Global Navigation Satellite System and Strapdown Inertial Navigation System (GNSS/SINS) serves as the primary navigation architecture for USVs. While the cost of high-performance GNSS receivers has steadily decreased, high-precision SINS remains prohibitively expensive. Consequently, micro-electromechanical system (MEMS)-based SINS has emerged as a preferred alternative due to its favorable balance of cost, power consumption, and size. However, significant inertial sensor errors make it difficult to maintain high-precision positioning during GNSS outages. To address this limitation, the single-axis rotational inertial navigation system (SRINS) has been introduced. Nevertheless, constrained by the single-axis mechanical structure and complex sea state disturbances, the system still struggles to effectively modulate random errors and azimuth gyroscope drift, rendering it insufficient for highly demanding navigation tasks. To overcome these bottlenecks, this article systematically reviews four core technologies: (1) Comprehensive denoising and temperature drift compensation techniques for MEMS gyroscopes; (2) rapid moving-base initial alignment models under high sea state disturbances; (3) fast online calibration methods for azimuth gyroscope drift; and (4) adaptive and robust GNSS/SINS integration architectures capable of accommodating high-dynamic conditions and non-Gaussian interference. Finally, this article discusses the engineering conflict between deploying high-precision algorithms and the limited onboard computational capacity of USVs. It concludes by highlighting a highly promising navigation paradigm for future research: the integration of factor graph optimization with physics-informed deep learning. Full article

Wireless sensor networks (WSNs) have increasing demand on lightweight, efficient, and secure encryption techniques for devices with limited resources, since traditional algorithms require high computation which make them impractical. This preliminary study presents an encryption algorithm based on chaos designed for transmitting short data, using the Lorenz system and Euler’s method for computation. It is combined with a synchronization model based on data array. It inserts iteration parameters within the ciphertext to ensure consistent key reproduction while decrypting. Within the broader context of e-health data streams, encryption efficiency is critical: continuous ECG signals generate large volumes of data that challenge real-time secure transmission, whereas individual blood pressure readings are far smaller and lightweight. While this work delimits its scope to short, low-power transmissions, simulations and hardware implementation on an nRF chip using the Enhanced ShockBurst (ESB) protocol demonstrated efficiency, with the lowest encryption speed of 0.154 ms for a 1-byte payload. Security analysis using the NIST Statistical Test Suite confirmed high statistical randomness of the generated keystream, and theoretical key-space analysis supports robustness. By focusing on short-stream encryption in preliminary form, the scheme contributes toward inclusive secure communication technologies for resource-constrained IoT healthcare systems and diverse user populations. Full article

Vibration energy is widely present in the natural environment. In the development of wearable self-powered systems, how to efficiently harvest the low-frequency mechanical energy of human motion has always been a core challenge. The piezoelectric–electromagnetic hybrid energy harvester designed in this paper consists of two units: a piezoelectric unit and an electromagnetic unit. The piezoelectric unit is composed of two arched plates, a piezoelectric layer, and an end magnet. The two sides of the piezoelectric unit are completely symmetrical. The electromagnetic unit is composed of a hollow tube, a central magnet, and a coil. The coil is wound around the outside of the center of the hollow tube to ensure that the central magnet can cut more magnetic flux lines. The two units output voltage through an external load. Firstly, based on a physical model, the force–electricity coupling mechanism is derived, and the dynamic response of the harvester at different frequencies is systematically tested. Secondly, through simulation and experiment, the influencing factors of the output voltage are deeply studied, and it is concluded that at medium and low frequencies (5 Hz–15 Hz), the harvester can provide efficient voltage output. The electromagnetic unit dominates at low frequencies and can output a larger voltage, but the voltage drops significantly after a certain frequency. The piezoelectric unit can supplement after the electromagnetic voltage drops, and the two have a synergistic effect. In addition, the output characteristics of the system mainly depend on frequency, initial distance, coil turns, and magnet mass. This paper clarifies the inherent physical mechanism of the hybrid energy harvester and provides an effective scientific reference for practical human motion energy conversion applications. Full article

Continuous monitoring of vehicle engine vibration is a key enabler of real-time diagnostics, yet conventional accelerometers require an external power supply and fit poorly into the distributed sensor networks envisioned for next-generation vehicles. Triboelectric nanogenerators offer an attractive self-powered alternative, but their direct application to the vibration of a running passenger vehicle engine, and the explicit link between sensor design parameters and individual engine operating states, remains largely unexplored. Here, we address this gap by co-tuning the air gap and the substrate rigidity of a contact-separation triboelectric vibration sensor to the vibration spectrum of an automotive engine. A systematic 3 × 3 design sweep across three gap distances and three substrate types identifies a single configuration that simultaneously resolves the low-frequency idle band and the higher-frequency acceleration band of a four-cylinder gasoline engine. A frequency-amplitude response map confirms that the real engine operating points fall within the sensitive region of the optimized device, and an on-vehicle test demonstrates clean discrimination of all seven operating states, from ready to shut-down, without any external power. The results establish design guidelines for source-matched triboelectric vibration sensors and outline a practical path toward self-powered, wireless-ready engine health monitoring in future vehicles. Full article

UAV-assisted data collection often suffers from spatial data holes and communication unfairness, a challenge exacerbated in Wireless Powered Communication Networks (WPCNs) by the inherent doubly near-far problem. To bridge these gaps, this paper proposes a novel Spatio-Temporal Trajectory-Driven Dynamic Time-Division Multiple Access (STD-TDMA) scheduling strategy. Deviating from conventional discrete hovering paradigms, we introduce a continuous-flight framework that exploits the UAV’s mobility to provide seamless spatial coverage. By jointly optimizing the UAV’s flight speed and dynamic time-slot allocation, the proposed strategy ensures that each sensor node can interact with the UAV at its optimal channel condition along the trajectory, thereby effectively mitigating the doubly near-far effect and ensuring quality of service-based fairness. To solve the formulated non-convex optimization problem, we develop a low-complexity algorithm that integrates Binary Search for speed optimization with the Hungarian algorithm for spatio-temporal mapping. Extensive simulations demonstrate that our STD-TDMA strategy significantly enhances nodal fairness and boosts overall task execution efficiency compared to conventional baseline schemes. Full article

Monitoring air quality is crucial for understanding and improving public health. There is interest in developing ultra-sensitive, low-power, cost-effective sensors. This work demonstrates that structural modulation of Sn nanoparticles through controlled deposition and oxidation enables a transition from metallic to semiconducting percolative networks, significantly enhancing NO₂ sensing performance at room temperature. The proposed percolation-driven sensing mechanism provides a new framework for understanding charge transport and gas interaction in nanostructured metal oxide systems. The nanoparticles are deposited near the percolation threshold for electrical conduction and, upon exposure to air, consist of a tin core and an amorphous Sn₃O₄ surface. Post-deposition heating in air at 320 °C for two hours forms SnO and Sn₃O₄ on top of the gold electrodes and polycrystalline SnO in the tetragonal litharge phase, known as Romarchite, on the glass between the electrodes. Both as-deposited and heat-treated sensors were capable of detecting NO₂ at room temperature, with a limit of detection in the parts-per-billion range. A percolation model is used to explain their operating currents, in which NO₂ reacts at nanoparticle gaps and intra-grain boundaries to form charge-depletion regions that primarily determine their resistance. Heat treatment has also been found to cause disproportionation of SnO, resulting in tin-rich precipitates and increasing the operating current to the milliampere range. These precipitates, although oxidized on their surfaces when exposed to air, may serve as bridges that reduce the total resistance of the percolating paths. Full article

Large-scale smart agriculture requires reliable and energy-efficient wireless connectivity to support distributed environmental sensing across wide rural areas. However, existing low-power wide-area network (LPWAN) technologies often face limitations in scalability, reliability, or infrastructure dependency when deployed in large agricultural fields. This study presents the design and experimental evaluation of a hierarchical sensor network architecture that integrates LoRaMESH for multi-hop sensing communication and Wi-Fi HaLow as a sub-GHz backhaul for data aggregation and cloud connectivity. In the proposed system, LoRaMESH forms intra-cluster sensor networks using a lightweight controlled flooding protocol, while Wi-Fi HaLow provides long-range IP-based connectivity between cluster gateways and a central access point. A real-world deployment covering approximately $2.5\mathrm{km}\times 1\mathrm{km}$ of agricultural area was implemented to evaluate the performance of the proposed architecture. Experimental results show that the LoRaMESH network achieves packet delivery ratios above $90\%$ across one to three hops, with average end-to-end delays between $10.6$ s and $13.3$ s. The Wi-Fi HaLow backhaul demonstrates high reliability within short to medium distances, reaching $99.5\%$ packet delivery ratio at 50 m and $89.68\%$ at 200 m. Energy measurements further indicate that the sensor nodes consume only $21.19\mathsf{\mu }\mathrm{A}$ in sleep mode, enabling long-term battery-powered operation suitable for agricultural monitoring applications. These results indicate that the proposed hierarchical architecture is a feasible connectivity option for the tested large-scale agricultural sensing scenario. Because no side-by-side LoRaWAN or NB-IoT benchmark was conducted on the same testbed, the results should be interpreted as a field validation of the proposed architecture rather than as a direct experimental demonstration of superiority over alternative LPWAN systems. Full article

Autonomous navigation technologies have been widely adopted in the automotive and aviation sectors, significantly reducing human-error-induced accidents and operational costs. However, their application to maritime systems remains limited due to the complexity of conventional propulsion systems. Electric propulsion ships, with well-defined system boundaries and accessible operational data, offer a promising platform for autonomous navigation. In this study, we propose an FMEA-guided selective multi-fidelity digital twin framework for fault detection, where model fidelity is adaptively selected between low- and high-fidelity models based on risk priority numbers derived from failure mode and effects analysis. This approach enables selective execution of computationally expensive models only under high-risk conditions, thereby improving computational efficiency. In addition, a sliding window-based algebraic aggregation method is employed to achieve lightweight and real-time fault diagnosis. The proposed framework is validated using operational sensor data from a 100 kW electric propulsion ship under multiple fault scenarios, including power supply faults and signal anomalies. Experimental results show that the proposed method reduces computational cost while maintaining stable real-time performance, compared to conventional data-driven AI-based approaches. These results demonstrate that the proposed framework provides an effective and efficient solution for enhancing the reliability and safety of autonomous ship systems. Full article

Accurate prediction of energy consumption is essential for the operation and charging management of battery-electric buses. Existing prediction studies are often constrained by incomplete or low-resolution input data, limiting their robustness under real-world operating conditions. This paper presents a high-resolution, sensor-rich energy consumption modeling framework using second-by-second operational data and tests on an electric bus fleet operating on Route 49 in Jinan, China. The dataset integrates synchronized measurements of vehicle kinematics, powertrain variables, and thermal conditions, providing a substantially more complete description of bus operation against previous studies. Boosting-based machine learning models are developed to predict the instantaneous power demand, and their performance is evaluated in comparison with a physics-based energy model and other variants of machine learning models. Results show that the data-driven boosting models demonstrate excellent explanatory power ($R^2$ values of up to 0.99 (training) and 0.95 (test)) and remain reliable under nonlinear operating conditions. Feature and SHAP analyses identify physically consistent energy drivers, supporting the applicability of the approach to real-world public transport operations. Full article

Optoelectronic motion capture systems provide objective and high-resolution measurements of upper limb kinematics. The hand-to-mouth movement is closely related to motor development in children. The “Rab Hand-to-Mouth protocol” (BTS Bioengineering) is widely used; however, its seated configuration constrains elbow posture and may limit the ecological validity of the movement. In this study, we propose a methodological adaptation of the protocol in a standing position to allow a more physiological elbow configuration and to increase the dynamic range of elbow and shoulder motion. The objective was to characterize kinematic patterns of the hand-to-mouth movement in typically developing children aged 4 to 9 years using this adapted setup. This study was designed as a descriptive analysis and does not aim to provide formal validation of the standing protocol against the original seated configuration. An observational study that included 40 children was conducted. Motion data were acquired using eight optoelectronic cameras (sampling frequency: 250 Hz) and 17 reflective markers placed on the trunk and upper limbs. Kinematic patterns and spatiotemporal parameters were computed using dedicated motion analysis software. No significant differences were observed between dominant and non-dominant limbs in spatiotemporal parameters, whereas kinematic differences were minimal and limited to trunk rotation, as identified by Statistical Parametric Mapping (SPM). Some isolated statistically significant associations with age were identified in specific spatiotemporal variables; however, these variables showed low coefficients of determination (R²), indicating limited explanatory power of age. Overall, kinematic parameters did not exhibit consistent age-related patterns. These findings provide preliminary descriptive data for hand-to-mouth kinematics in a standing condition, which may contribute to the future development of assessment protocols. However, the limited sample size and the absence of pathological populations restrict the direct generalization of these findings. Future studies should evaluate the applicability of this approach in clinical cohorts and explore its integration into sensor-based and data-driven models for movement analysis. Full article

Event cameras are bio-inspired vision sensors offering low latency, low power consumption, and high dynamic range, capturing motion with microsecond-level precision via a per-event triggering mechanism. Despite these advantages, the inherent sparsity and lack of color in event data hinder direct analysis, necessitating advanced deep learning approaches. To achieve low-latency and high-precision motion segmentation for indoor robotic applications, this paper introduces a dual-branch decoupled CNN framework. Specifically, Principal Component Analysis (PCA) is utilized to project 3D event point clouds into 2D motion trend maps, capturing local motion priors while suppressing ambiguity in structured environments. Concurrently, an Event Leaky Integration (ELI) model, inspired by biological membrane potentials, is designed to enhance the structural representation of sparse events. Within this framework, separate branches respectively perform motion validation and shape extraction and are fused via a Spatial Gated Fusion (SGF) module to suppress static background interference. It is demonstrated experimentally that with an input window of only 10 ms, the proposed method achieves a 77% average mIoU across five indoor test scenarios from the EV-IMO dataset with an inference latency of 10 ms per frame. Compared to state-of-the-art methods like MSRNN and GCN, which required 30–300 ms event slices, our framework achieves a favorable trade-off between computational efficiency and segmentation accuracy, maintaining competitive performance under ultra-short time windows for indoor event-based motion processing. Full article

Accurate measurement of streamflow is fundamental for water resources management, ecological conservation, flash flood early warning, and climate change impact studies. This study presents a proof of concept on the usage of Internet of Things (IoT) for automatic streamflow measurements using commercial off-the-shelf (COTS) hardware. The system is designed, implemented, and experimentally evaluated as a low-cost, solar-powered IoT device tailored to small-order streams and headwater tributaries. At its core is the Hall-effect YF-S201 flow sensor. Although primarily designed for closed-conduit applications, the sensor was tested in a controlled setup where stream water was diverted into a short pipe section, enabling continuous monitoring and calibration. This paper provides details on the design and validation of a low-cost (approximately 24 Euros), solar-powered streamflow measurement system based on a water flow sensor, using wireless communications, and cloud storage based on an ESP32 board, PostgreSQL, and a web interface. The device was tested in a simulated environment. Results indicate the proposed device reliably tracks flow variability, while offering portability, energy autonomy, and cost efficiency, and may serve as a feasible alternative for low-infrastructure, temporary deployments. Full article

In the context of an increasingly chaotic electromagnetic environment, the problem of multisensor data fusion for tracking airborne maneuvering targets has garnered significant attention and applications. In low-to-no power scenarios, certain sensors exhibit measurement inaccuracies, and the disparity in measurement precision among networked sensors leads to data inequality. This results in poor fusion accuracy in the multisensor fusion process, particularly when prior weights are unknown. To address the aforementioned problems, this study first redefines the motion model of airborne maneuvering targets by capturing the complexity of the trajectory of the target. Subsequently, a modeling framework for low-to-no power scenarios is established using a one-transmitter three-receiver radar system. In this model, the Signal-to-Noise Ratio (SNR) of the two sensors was intentionally reduced to simulate data inequality. Finally, a distance velocity (DV) fusion algorithm was designed based on the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) algorithm. Specifically, after the state extraction step of the SMC-PHD filter algorithm, the final estimated target was obtained in two steps: judgment and weighted summation. The simulation results demonstrate the effectiveness of the proposed algorithm in improving fusion accuracy and robustness in dynamic environments and under real electromagnetic interference. Full article

Aiming at the technical pain points of traditional manhole covers with low intelligence high cost and excessive power consumption, this study designs a TENG-based alarm device to enhance the safety and maintenance efficiency of urban infrastructure. The device integrates a water immersion sensor and a displacement sensor enabling real-time status monitoring through a unique TENG mechanism. The solid–liquid mode water immersion sensor detects seepage through the triboelectrification effect. Water droplets contact electrodes on the surface of FEP film and generate electric energy to trigger the detection circuit. The displacement sensor adopts the independent layer mode of TENG and combines with a mechanical tumbler mechanism to realize displacement detection. External force-induced manhole cover displacement drives internal balls to roll and rub against electrodes. Electric energy is then generated to activate the detection circuit. On the basis of the two sensors, an efficient and reliable intelligent alarm system is constructed. The system receives and analyzes displacement and water immersion-sensing signals in real time. It rapidly identifies potential safety hazards including displacement offset water accumulation and leakage. Signal analysis and early warning prompts are completed synchronously. This system provides accurate and real-time data support for public facility monitoring, pipe network operation and maintenance, and regional security in smart cities. It helps achieve early detection and early disposal of hidden dangers and improves the intelligent and refined level of smart city monitoring. Full article

Single-chip integration technology for multifunctional sensors has become an important development direction due to its low power consumption and versatile functionality. However, the fabrication compatibility between different sensing components remains a key challenge for high-performance integrated sensors, often leading to complex processes and increased costs. This work presents a microfabrication-compatible photo-magnetic integrated sensor chip based on micro–nano processing methods. The integrated sensor chip includes giant magnetoresistance (GMR) and a light-dependent resistor (LDR). The fabrication process was based on standard MEMS fabrication with compatibility and cost-effectiveness. The experimental results demonstrated that the chip can simultaneously realize both optical and magnetic detection with magnetic field sensitivity of 3.74 mV/Oe and photodetection sensitivity of 0.79 $\mathsf{\mu }$A/($\mathsf{\mu }$W/cm²) at a 5 V bias. The integrated sensor features high-sensitivity magnetic performance and weak-light detection capability, with promising application in robotics and advanced manufacturing fields. Full article