Search Results (2,587)

Accurate nanoscale displacement readout is essential for optical inertial sensors, precision positioning, and micro-vibration characterization. In this work, we develop a free-space optical heterodyne interferometric readout system for low-frequency nanoscale displacement sensing and establish an SNR-guided adaptive demodulation framework. Two complementary demodulation strategies are integrated: Bessel-function-based frequency-domain sideband extraction for small-amplitude low-SNR motion and IQ quadrature phase tracking for larger-amplitude displacement. The experimentally demonstrated framework maps the applicability regimes of the two methods and enables wavelength-referenced displacement readout over a range from sub-nanometer narrowband detection to 250 nm under the present experimental conditions. The implemented system achieves a repeated-measurement repeatability of 0.40 nm under a 10 Hz excitation condition, and spectral SNR analysis is consistent with time-domain statistical evaluation. Finally, the readout system is applied to a quartz pendulum inertial structure, demonstrating its potential for photonic displacement sensing and optical inertial sensor characterization. Full article

Scene understanding is a fundamental task in autonomous driving, requiring effective integration of semantic and geometric information from heterogeneous sensors. Although vision–language models (VLMs) provide powerful semantic representations, their integration with LiDAR-based geometric perception remains challenging. This paper proposes a multimodal late-fusion framework for multi-label scene classification that combines semantic embeddings extracted from camera images using a frozen CLIP (ViT-B/32) encoder with geometric features derived from LiDAR Bird’s-Eye-View (BEV) representations. To improve multimodal compatibility, modality-specific adaptation networks are employed to refine visual and geometric features before fusion. The proposed framework was evaluated on an annotated subset of the nuScenes dataset containing synchronized camera–LiDAR samples and nine scene-level labels. Experimental results show that the proposed late-fusion architecture outperforms both unimodal and early-fusion baselines, achieving a Hamming Accuracy of 0.950, a Micro-F1 score of 0.925, and a mean Average Precision (mAP) of 0.908. Additional experiments using a CLIP-based early-fusion baseline demonstrate that the observed performance gains are primarily attributable to the proposed modality-specific refinement and late-fusion strategy rather than the visual encoder alone. These findings indicate that modality-aware late fusion of pretrained semantic representations and LiDAR geometric information provides an effective and scalable solution for multimodal perception in autonomous driving. Full article

As the core sensor of inertial measurement units, the reliability of Micro-Electro-Mechanical Systems (MEMS) gyroscopes is critical for long-term navigation and motion control applications. To bridge the mechanism-data gap in MEMS multi-mechanism degradation modeling, this paper proposes a physics-informed dual-indicator reliability assessment framework based on Wiener processes. Two degradation indicators under consideration are frequency-related degradation caused by stiffness degradation and Q-factor degradation caused by damping degradation, for which corresponding physics-embedded stochastic degradation models are formulated. The two indicators are normalized and fused through a generalized weighted limit state function, where failure is defined as gyroscope-level performance failure. Closed-form reliability expressions are derived for linear limit states, while Monte Carlo simulation is used for nonlinear cases. Reduced-order multiphysics simulation cases, including a double-ended fixed beam and a cantilevered MEMS mass block, are used to demonstrate the mechanism-to-indicator-to-reliability modeling procedure. The results show that the proposed dual-indicator framework provides more balanced reliability assessment than single-indicator analysis under the simulation setting. The proposed method offers an alternative mechanism-informed approach for reliability analysis and lifetime prediction of other MEMS devices. Full article

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The system integrates a u-blox ZED-F9P RTK-GNSS receiver, a Bosch BNO085 9-DoF IMU with on-chip sensor fusion, a CAN-FD interface for powertrain data acquisition, and a SIM7600E-H 4G/LTE module for real-time remote streaming, all housed in a 3D-printed vibration-resistant enclosure. The firmware employs deterministic dual-core task partitioning: the Cortex-M7 core handles sensor fusion and CAN-FD at high frequency, while the Cortex-M4 core manages 4G communication and microSD logging. We explicitly delimit the scope of the evidence presented: CAN-FD powertrain acquisition and end-to-end operational reliability are experimentally validated on real circuit data spanning four campaigns, over 100 laps, and 5.8 h of logging—with sustained acquisition of 13 powertrain channels at speeds up to 185 km/h and zero system resets or data-integrity errors. In contrast, RTK positioning accuracy (2.5 cm CEP), sensor-fusion latency (sub-2 ms at the 99th percentile), 4G-uplink reliability, and thermal margins are characterized through manufacturer specifications, Monte Carlo simulation, and analytical models, with a fully instrumented end-to-end measurement campaign identified as the immediate next step. The 50 Hz effective positioning rate combines 25 Hz GNSS with IMU interpolation. With a bill of materials of approximately EUR 265, the platform offers an order-of-magnitude cost reduction over commercial alternatives while providing full openness and extensibility for distributed intelligence applications. Full article

Micro quadrotor unmanned aerial vehicles (UAVs) are highly sensitive to external disturbances and model uncertainties because of their small mass, low moment of inertia, and limited onboard computational resources. To improve the disturbance rejection and trajectory tracking performance of micro quadrotor UAVs, this paper proposes an event-triggered extended state observer (ET-ESO)-based prescribed-time funnel control (PTFC) method. First, a control-oriented dynamic model of the micro quadrotor is established, in which wind disturbances, unmodeled aerodynamic effects, damping uncertainties, and parameter perturbations are represented as lumped disturbances in the translational and rotational subsystems. Then, two event-triggered ESOs are designed to estimate the lumped disturbances of the velocity and angular velocity channels. Compared with conventional continuously sampled ESO schemes, the proposed event-triggered mechanism reduces the frequency of sensor-to-controller information transmission while preserving disturbance estimation capability. Furthermore, a prescribed-time funnel control law is developed to constrain the position and attitude tracking errors within predefined performance boundaries and ensure convergence to the desired accuracy region within a user-specified time. Lyapunov-based stability analysis is provided to prove the boundedness of all closed-loop signals and the validity of the prescribed funnel constraints. Finally, MATLAB/Simulink simulations based on the Parrot Mambo mini-drone parameters are conducted to verify the effectiveness of the proposed method. The results demonstrate that the proposed controller achieves robust trajectory tracking, effective disturbance compensation, improved transient performance, and reduced control update frequency. 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

Facing global problems such as the energy crisis and climate change, in recent years, the bioelectrochemical system represented by plant microbial fuel cell (PMFC) has been widely studied. It is a frontier bioelectrochemical technology that combines plant photosynthesis, rhizosphere microbial metabolism, and electrochemical energy conversion. This paper focuses on the linkage application of PMFC and intelligent sensing technology in the paddy-field environment, systematically expounds the basic composition, working principle, and integration mode of this technology with paddy field ecology, and emphatically analyzes its realization path and application potential in self-powered external sensor deployment, rhizosphere biosensor, and multi-node sensor network integration. The analysis shows that PMFC has the unique advantage of in situ and continuous micro-power generation in flooded rice fields. Its output not only supports the intermittent operation of low-power sensors, but the output electrical signals can also reflect plant stress and environmental conditions, thereby possessing biosensing potential. However, the current system still faces key bottlenecks, such as low power density, easily disturbed electrical signals, and high cost of high-performance electrode materials, which restrict the actual deployment of rice fields. Through the collaborative optimization of electrode interface engineering, microbial community directional control, and low-power sensing fusion strategy, it is expected to promote the transformation of PMFC from principle verification to field intelligent monitoring application. Full article

Characteristic gas-based detection technology can facilitate the warning of lithium-ion battery thermal runaway with a high accuracy at an early stage. Microelectromechanical system (MEMS) metal–oxide–semiconductor (MOS) gas sensors have advantages of a low cost, a high accuracy, and low power consumption; therefore, they are ideal candidates for the lithium-ion battery thermal-runaway warning. MEMS MOS gas sensors are composed of a micro-hotplate and gas-sensitive materials. The micro-hotplate component strongly influences the device’s mechanical and thermal properties. Initially, we used COMSOL to optimize the micro-hotplate component. Then, we fabricated the device based on the optimal micro-hotplate. Next, gas-sensitive materials made of ZnO and ZnO-Au were deposited on the micro-hotplate by radio-frequency magnetic sputtering. The self-made and commercial MEMS MOS sensors were integrated to form an electronic nose. The as-made electronic nose can classify hydrogen, ethylene, acetylene, methane, carbon monoxide, and ethanol with a maximum accuracy of 99.4% using gas response data acquired over only 20 s. The reported work can provide a solution for an early and accurate lithium-ion battery thermal runaway warning. Full article

Continuous manufacturing has emerged as the prevailing paradigm in the modern pharmaceutical industry, imposing stringent demands for efficient, real-time inspection methods. Furthermore, deploying high-performance deep learning models on industrial edge devices remains challenging due to computational constraints and the difficulty of detecting micro-defects (e.g., micro-cracks and spots). This paper proposes EMN-net, a lightweight defect detection model built upon the YOLOv8n architecture. The proposed algorithm integrates a MobileNetV3 backbone, the Efficient Local Attention (ELA) mechanism and the Normalized Wasserstein Distance (NWD) loss function to balance computational efficiency with sensitivity toward micro-defects. Evaluated on a self-built industrial tablet dataset expanded to 3086 images, EMN-net achieves an mAP50 of 97.8%, representing a 2.5% improvement over the baseline YOLOv8n. the computational complexity is reduced to 4.4 GFLOPs, while the inference throughput reaches 118 FPS, satisfying the real-time requirements of high-speed production lines. Additionally, the model exhibits improved robustness under simulated motion blur and sensor noise. EMN-net presents a balanced automated visual inspection solution for edge devices in continuous pharmaceutical manufacturing. Full article

Firefighting and urban search operations occur in hazardous, rapidly changing environments where timely situational awareness is critical. In indoor firefighting scenarios, responders often operate in smoke-filled and structurally complex environments with limited visibility and communication. While UAVs have been widely used in wildfire response, their deployment inside buildings remains limited due to constraints in system mass, cost, and operational complexity. This paper presents the design and preliminary validation of an attritable micro-UAV as a proof-of-concept platform for indoor search support and post-fire inspection and assessment. The platform emphasizes portability, durability, and multi-sensor integration, enabling deployment by minimally trained personnel. System requirements were derived in collaboration with the Southfield Fire Department. The finalized design achieved a total mass of 247.34 g at a cost of $2969. Experimental evaluation demonstrated reliable sensing and communication performance at the subsystem level and confirmed structural robustness through drop tests from heights up to 3 m. Endurance testing yielded a maximum flight time of 28 min, slightly below the targeted 30 min requirement. While full task-level validation in operational firefighting scenarios has not been conducted, the proposed platform establishes a foundation for future development, including system-level validation, post-fire structural assessment, and enhanced visualization interfaces for improved situational awareness in emergency response operations. Full article

This paper presents the process of gathering data for a flapping-wing micro air vehicle (FWMAV) using optical tracking and force sensors for subsequent dynamic modeling and simulation purposes. Tethered and clamped experiments were performed to track the vehicle’s overall motion, wing kinematic angles, and aerodynamic force patterns, while additional properties such as mass, inertia tensor, center-of-mass position, and short-period excitation frequency were also examined. The methodology includes the testing approaches, modeling choices, and error analyses applied to the measurements. The results demonstrate that both tethered and clamped configurations introduce key limitations, particularly for steady-state flight. Additional constraints include structural fragility (hindering high-frequency testing), over-simplified CAD geometry, and controller tuning issues on the tail. Based on the identified parameters and experimental datasets, a high-fidelity simulation model was developed in MATLAB to serve as a platform for future control and flight envelope studies. Overall, the combination of optical tracking and force sensing provides a structured framework for linking experimental data to physical models, laying the foundation for future improvements in ornithopter modeling and testing. Full article

Self-healing materials have attracted increasing attention as a strategy to enhance durability, extend service life, and reduce maintenance in advanced material systems. Among these, cellulose-based self-healing materials represent a sophisticated intersection between sustainable macromolecular chemistry and adaptive materials science. This review provides a synthesis of recent advancements in the field, systematically categorizing materials derived from cellulose raw materials. We evaluate the fundamental chemical strategies employed to achieve autonomous repair, distinguishing between extrinsic mechanisms—utilizing cellulose-based micro/nano-capsules to sequester healing agents—and intrinsic mechanisms governed by dynamic covalent chemistry (Schiff-base, boronic ester, Diels–Alder) and supramolecular interactions (hydrogen bonding, metal–ligand coordination, and host–guest assemblies). The analysis highlights how cellulose’s hierarchical structure and abundant surface functionality are leveraged to overcome the traditional trade-off between mechanical toughness and healing efficiency. Particular emphasis is placed on the transition from simple structural hydrogels to sophisticated multifunctional systems. These include ultra-stretchable strain and pressure sensors for e-skin applications, biocompatible and injectable matrices for chronic wound management and stem cell delivery, and advanced anti-freezing eutectogels for performance in extreme environments. Furthermore, we explore the integration of cellulose into traditional sectors, such as self-healing concrete utilizing microbe-induced calcification and smart, eco-friendly coatings for corrosion protection. Finally, we discuss critical challenges, including environmental stability, scalability, and the development of standardized evaluation protocols, providing a roadmap for the next generation of bio-derived, sustainable and intelligent materials. Full article

Micro- and nanoscale mass sensing is crucial for applications such as molecular detection and wearable monitoring. However, the observation of mass perturbations in flexible composite structures requires systematic theoretical evaluation. This study develops a dual-mode vibration-based mass-sensing model based on a film–substrate composite strip. By releasing and re-stretching pre-strain in the soft substrate, the ribbon can reversibly switch between a two-dimensional flat configuration (Mode 1) and a three-dimensional buckled configuration (Mode 2), leading to distinct dynamic responses. Under a finite-deformation Euler–Bernoulli beam assumption, displacement fields and kinematic relations are formulated for both configurations. An energy-based approach is employed to decompose the total energy into stretching and bending contributions, while an added-mass block is incorporated into the kinetic energy as a lumped mass. The governing equations of motion are derived using the Lagrange equations and the Hamiltonian function. Based on these results, the influence of the added mass on displacement signatures is examined, and the mode-dependent observability in the flat versus buckled states is compared, providing an analytical basis for mass sensor evaluation. Full article

Low-temperature conditioning is a key procedure in the flexibility evaluation of waterproofing membranes and directly affects the reliability of subsequent performance assessments. However, the internal unsteady-state heat transfer kinetics and the thermal gradient evolution mechanisms in multi-layer composite membranes under transient cold shocks require further investigation. Focusing on commonly utilized 3 mm and 4 mm thick SBS (Styrene–Butadiene–Styrene)-modified bitumen waterproofing membranes as subjects, this study investigated the internal dynamic temperature fields and microstructural response of bituminous waterproofing membranes under standard low-temperature flexibility testing conditions. By accurately pre-embedding micro-temperature sensors in situ at the interface between the surface layer and the reinforcement matrix, the transient thermal response profiles of specimens with varying specifications in a −25 °C liquid environment were quantified. Simultaneously, a three-dimensional transient heat conduction finite element model was established to elucidate the dynamic evolution of internal spatial temperature gradients. The congruence between experimental and numerical results demonstrates that upon exposure to extreme cold, composite membranes of different thicknesses exhibit a pronounced “surface-to-core” heat transfer lag effect. The cooling rate maximized within the initial 10 min of exposure. Conversely, the internal interface layer—acting as a high-thermal-resistance zone and the most unfavorable point for heat conduction—necessitated 10~20 min of nonlinear thermal dissipation to stabilize at the target ambient temperature. This study clarifies the transient thermal response and temperature-field evolution laws of bituminous waterproofing membranes, providing a robust theoretical framework for elucidating low-temperature embrittlement mechanisms and informing the material design and application of waterproofing projects in cold regions. Full article

Brain-on-a-chip (BOC) refers to a miniaturized in vitro platform that integrates living neuronal networks on a micro-engineered chip, enabling the simulation of brain functions, neural activities and physiological responses. BOC technology is an advanced evolution of microphysiological systems (MPS) and Lab-on-a-Chip platforms, providing novel paradigms for in vitro modeling and exploring early-stage biocomputing by interfacing living neural networks with engineered electronics. Microelectrode arrays (MEAs) serve as the critical physical interface for bidirectional communication in these systems. In this review, we systematically examine the technological landscape and engineering requirements of MEAs tailored for BOC applications, evaluating them across electrical characteristics, structural properties, and biocompatibility. Two primary classes of current MEA technologies, including planar arrays for 2D neural cultures and 3D flexible arrays for brain organoids, are discussed in detail. We highlight the transition from passive planar electrodes to high-density active CMOS and TFT-based arrays, and detail how 3D flexible MEAs utilize endogenous integration and exogenous wrapping strategies to overcome tissue-mechanics mismatches. Furthermore, the integration of MEAs with microfluidics, optoelectronics, and electrochemical sensors to enable multimodal monitoring is explored. With the advantages of the various MEAs, the application of MEAs for BOC, particularly in biological computing and network plasticity research, is discussed. Finally, future technological developments in scalability bottlenecks, chronic stability, and the incorporation of artificial intelligence for MEAs of BOC are prospected. Full article