Search Results (1,932)

Surface quality is a critical indicator of product performance, creating a growing demand for real-time surface inspection in industrial manufacturing. However, conventional surface normal measurement techniques require sequential measurements with varying illumination or observation angles, making them unsuitable for high-speed online inspection. To overcome this limitation, this paper proposes a novel imaging technique, termed normalography, for visualizing pixel-wise surface normal distributions. Analogous to thermography, normalography visualizes the spatial distribution of surface normal directions over a material surface. The proposed method targets highly glossy and smooth surfaces and is based on reflectance measurements. A multi-angle collimator was developed to simultaneously illuminate the target surface from multiple incident directions, while multispectral illumination was employed to distinguish the reflected light corresponding to each direction. An imaging system incorporating red, green, and blue illumination sources enables single-shot acquisition of surface normal information over a 1024 × 1024-pixel field of view. The proposed normalography enables camera-like real-time visualization of surface normal distributions without sequential image acquisition, demonstrating its potential for online surface inspection and quality monitoring in industrial manufacturing. Full article

Unauthorized unmanned aerial vehicle (UAV) intrusions in sensitive areas such as airports have made accurate UAV detection and localization a pressing need. Acoustic sensing is passive and weather-independent, but conventional microphone arrays require many elements and a large aperture. This paper proposes an acoustic vector sensor (AVS)-based method, termed Covariance Enhancement and Confidence-guided Tracking for 3D Acoustic Localization (CECT-3DAL). A single AVS measures the sound pressure and three-axis particle velocity at one point. Adaptive diagonal loading improves the robustness of the covariance matrix at a low signal-to-noise ratio (SNR). An exponential spectral enhancement strategy sharpens the spatial spectrum peaks for direction estimation, and an eigenvalue-ratio-based confidence drives confidence-weighted smoothing of the angle sequences. Meanwhile, a dual-sensor geometric model provides a closed-form three-dimensional solution. In simulations, the azimuth and elevation root-mean-square errors (RMSEs) were below 1.5° for SNR above 4 dB. In an anechoic chamber, confidence-weighted smoothing reduced the azimuth and elevation standard deviations from 4.34° and 2.63° to 1.46° and 0.86°. In field experiments, the hovering azimuth stayed within a 90% span of 2–3.5°, with an average horizontal RMSE of 0.209 m against a GPS reference, and trajectories under various flight modes remained continuous and smooth. The proposed method offers a compact, passive, and low-cost solution for counter-UAV acoustic surveillance. Full article

Conventional quasi-bound state in the continuum (quasi-BIC) terahertz (THz) metasurfaces usually operate at fixed resonance frequencies and lack electrically controlled dynamic tunability. To address this limitation, we propose two schemes for realizing liquid-crystal-integrated quasi-BIC THz metasurfaces by exploiting the electrically tunable refractive index of liquid crystals in the THz regime. A microfluidic multilayer architecture combining a liquid-crystal functional layer with a silicon (Si)-based metasurface is constructed, in which the transition from an ideal BIC to a quasi-BIC is realized through two distinct mechanisms: pixelated control of the liquid-crystal orientation and geometric symmetry breaking of the Si resonators. Cartesian multipole decomposition reveals that the resonances in both configurations are dominated by magnetic dipole modes. The effects of the liquid-crystal orientation angle and key geometric parameters on the resonance frequency, peak absorptance, and quality factor are systematically investigated. Numerical results demonstrate continuous tuning of the terahertz resonance through variation in the liquid-crystal orientation angle, corresponding to electrically driven liquid-crystal reorientation in practical devices. The sensing performance of the optimized Si-resonator-based configuration is further evaluated, achieving a refractive index sensitivity of 117 GHz RIU⁻¹ and a figure of merit (FOM) of 146.25. This work overcomes the fixed-frequency limitation of conventional static quasi-BIC metasurfaces and provides a feasible route toward electrically reconfigurable terahertz microfluidic sensing devices with tunable operating frequencies. Full article

Tear analysis plays a crucial role in the early screening and diagnosis of ophthalmic diseases. However, conventional methods are often limited by poor real-time performance, low portability, and insufficient capability for multi-parameter detection. Here, we present a bioinspired triboelectric electronic tongue integrated with a microfluidic chip for multimodal detection of tear pH and disease-related biomarkers. The system combines three triboelectric nanogenerator (TENG) modes, including droplet-based, dual-electrode sliding, and single-electrode sliding configurations. The droplet-based TENG converts gravitational potential energy into electrical energy, generating a maximum output voltage of 65 V. The sliding TENG further expands the sensing dimensions by characterizing droplet flow behavior and viscosity-related properties. Benefiting from the high sensitivity of the dual-electrode mode and the waveform differentiation capability of the single-electrode mode, the platform enables enhanced sample discrimination. After optimizing key parameters, including droplet height and chip inclination angle, the output stability errors for all three TENG modes were maintained within ±10%. Combined with a random forest algorithm, the multimodal sensing system achieved a classification accuracy exceeding 96.6% for artificial tears with different pH values. Moreover, distinct electrical response patterns were observed for ophthalmic disease-related biomarkers, including Lysozyme, Interleukin-6 (IL-6), and Chlamydia, demonstrating excellent type identification and concentration detection capability. This work provides a self-powered and miniaturized strategy for intelligent tear analysis and multiple-parameter sensing, offering significant potential for ophthalmic disease diagnosis. Full article

In deep-space exploration and remote sensing, characterizing radiative transfer in complex planetary atmospheres is fundamental for robust target detection and optical navigation. On the Martian surface, intense scattering and attenuation by dust aerosols pose severe environmental interference, challenging star sensors used for high-precision navigation. To address this, this study develops a spectral radiative transfer model based on the Null Collision Monte Carlo Method to characterize the optical background of the dusty Martian atmosphere. Mie scattering theory is employed for dust particles, while gas molecular absorption is modeled via line-by-line integration. The simulated sky radiance is validated against Mars rover Navcam observations, yielding an average relative error of 7.83% between the modeled and observed radiance values across scattering angles greater than 5°. Building on this, an imaging link model evaluates surface-based detection performance, including signal-to-noise ratio, detection success probability, and star count. Optical parameters—aperture, field of view, and integration time—are optimized for nighttime and dawn-dusk modes. Spatio-temporal assessments are conducted globally across Martian years, focusing on the Zhurong landing site and Tianwen-3 candidates. Finally, an Earth-environment equivalence experiment using a 60% transmittance filter verifies the design’s robustness. This work confirms the feasibility of star-sensor-based attitude determination on Mars. Full article

Smartphones incorporate acoustic components, including a speaker and multiple microphones, which can be used as a low-cost, contactless platform for vital signs monitoring. However, extracting breathing rate (BR) and heart rate (HR) from smartphone acoustic reflections remains challenging in indoor environments because thoracic reflections are weak and are often mixed with static clutter, hand motion, environmental multipath, and other dynamic sources. In this work, we present a smartphone-based frequency-modulated continuous wave (FMCW) acoustic sensing system that enables simultaneous BR and HR estimation using the integrated speaker and two physical microphones. Instead of processing the received signal as a single, mixed signal, the proposed method leverages distance information from the FMCW beat frequency and an angular phase index (AoA information), derived from dual-microphone and virtual aperture processing, to organize moving reflectors into a joint distance–angle–time representation. A 3D-DBSCAN clustering module is then applied to this representation to separate candidate dynamic sources from static and multipath components, without presupposing the number of sources. To further handle ambiguous cases where multiple candidate dynamic sources are detected, a Siamese similarity network is introduced as a conditional second-stage source-association module. The Siamese model compares candidate thoracic waveforms and estimates whether multiple detected components are likely to originate from the same physical source or different sources, thus improving source selection without resorting to classical blind source separation. The system was evaluated on 20 participants in two indoor environments, a laboratory and a bedroom, using three consumer smartphones and an electrocardiogram (ECG) reference device. In the smartphone-only blind configuration, the proposed pipeline achieved MAEs of 2.312 bpm for HR and 1.394 bpm for BR. In the ECG-assisted calibrated configuration, which is used to evaluate physiological coherence rather than deployable smartphone-only performance, the errors decreased to 0.462 bpm for HR and 0.091 bpm for BR. These results demonstrate that spatial clustering and conditional Siamese source pairing improve the robustness of acoustic vital sign detection using smartphones in indoor environments. Full article

Canopy temperature (T_c) is an important indicator for characterizing crop water status and serves as the core variable for constructing the Crop Water Stress Index (CWSI). Timely and accurate diagnosis of crop water stress is of great significance for precision irrigation and yield improvement. Owing to its non-contact and high-efficiency characteristics, unmanned aerial vehicle (UAV) remote sensing has become an effective approach for high-spatiotemporal-resolution monitoring of crop water conditions. However, variations in observation geometry can introduce thermal directional effects in canopy temperature, thereby reducing the stability and reliability of CWSI estimation. In this study, multi-angular thermal infrared imagery acquired by a UAV platform was utilized to investigate the directional characteristics of winter wheat canopy temperature. A kernel-driven model was employed to separate the directional components of canopy temperature and retrieve isotropic temperature parameters that more closely represent the actual thermal status of the crop canopy. Based on these temperature parameters, three CWSI models were constructed and evaluated for crop water stress diagnosis. The results demonstrated that (1) winter wheat canopy temperature exhibited pronounced directional characteristics, and the observed temperature generally decreased with increasing relative azimuth angle between the viewing direction and solar incident direction; (2) after angular correction, the isotropic canopy temperature simulated by the kernel-driven model showed an improved correlation with soil moisture content at a depth of 30 cm (R² = 0.54); and (3) when angular-corrected canopy temperature was used as the input variable for different CWSI models, the sensitivity of all models to crop water variation was substantially enhanced, resulting in improved discrimination among different irrigation treatments. Among the evaluated approaches, the empirical CWSI model achieved the best performance in diagnosing crop water stress variations (R² = 0.73, RMSE = 1.59%). These findings provide a theoretical basis for UAV-based thermal infrared remote sensing of crop water status and offer technical support for precision irrigation management. Full article

Soil microplastics undergo aging, but how aging modifies their effects on soil bacterial communities remains unclear. Here, we conducted a 180-day incubation experiment with no PVC (CK), pristine PVC microplastics (IP), and UV-aged PVC microplastics (AP, 0.5%, w/w). UV aging markedly altered PVC surface properties: roughness increased from approximately 12.9 to 21.8 nm, water contact angle decreased from 91.44° to 82.38°, and the O/C ratio increased from 0.37 to 0.43. Bacterial richness indices were largely unchanged, whereas Shannon diversity decreased under AP, indicating reduced community evenness. Bray–Curtis analysis showed significant community separation among treatments (PERMANOVA: R² = 0.364, p = 0.003), with UV aging further altering the trajectory of PVC-induced community reorganization. At the genus level, AP was associated with enrichment of Methylobacillus and lower robustness in exploratory co-occurrence network analysis, suggesting a distinct bulk-soil bacterial response compared with IP. Functional prediction further suggested that AP and IP were associated with different predicted pathway profiles, with AP showing higher predicted representation of pathways related to carbon metabolism, respiratory energy metabolism, potential prokaryotic carbon fixation, environmental sensing, cellular maintenance, and antimicrobial-resistance-associated categories, whereas IP was mainly associated with transport- and communication-related predicted functions. These predicted functional patterns require further validation using metagenomic, qPCR, transcriptomic, biochemical, or chemical approaches. Overall, these findings highlight the need to consider the UV aging status of PVC microplastics when evaluating their effects on soil bacterial communities. Full article

To address the bias in radiative simulation caused by the horizontal inhomogeneity of atmospheric parameters under large scan angles for the HIRAS-II (Hyperspectral Infrared Atmospheric Sounder-II) onboard the Fengyun-3E satellite, this study proposes a method for constructing slant-path atmospheric parameter profiles along the satellite’s line-of-sight (Exp.2). In contrast to the conventional method (Exp.1) based on the assumption of horizontally homogeneous vertical atmospheric profiles, this method accurately calculates the intersection points between the satellite’s line-of-sight and the various altitude layers of the ECMWF reanalysis data version-5 (ERA5). It employs a hybrid interpolation algorithm combining inverse distance weighting and spline interpolation to obtain a continuous distribution of atmospheric parameters along the slant path, thereby accounting for the actual observation geometry of the satellite. The results show that when the satellite zenith angle exceeds 30°, the simulation differences between Exp.2 and Exp.1 increase significantly. Specifically, in the CO₂ absorption band, the differences between the two methods are mainly concentrated between −0.1 K and 0.1 K, whereas in the water vapor absorption band, this range expands to between −0.5 K and 0.5 K. Notably, biases are concentrated near the scan edges and exhibit a strong latitudinal dependence, with high-latitude areas showing more prominent deviations due to steeper atmospheric parameter gradients and asymmetric orbital geometry. Moreover, a comparison with the measured satellite-observed brightness temperature further demonstrates that Exp.2 effectively reduces simulation biases near the scan edge. The mean bias is reduced by up to 0.1 K in water vapor absorption channels, a more significant improvement compared to the 0.03 K reduction in CO₂ absorption channels. These results indicate that the proposed slant-path profile construction method significantly enhances the accuracy and reliability of infrared hyperspectral radiative transfer forward simulations under complex observation geometries by providing a more realistic representation of the three-dimensional slant-path radiative transfer process. This advancement holds important implications for improving the atmospheric correction of remote sensing data. Full article

This paper tackles the problem of performance limitations on channel estimation accuracy in near-field massive MIMO systems resulting from the sparse reconstruction method with fixed spatial grid dictionaries. It presents a deep unfolded network with a learnable sparse dictionary (LSD-UnfoldNet) to address the grid mismatch, thereby obtaining high accuracy and low-complexity near-field channel estimation as the sparse dictionary and channel reconstruction parameters are jointly optimized using end-to-end learning. The iterative shrinking threshold algorithm is unfolded into an L-layer neural network with L learnable sparse dictionaries and linear transformation matrices in each layer of the network. Besides, to improve the correlation characteristics of the sensing matrix, an orthogonal regularization term is introduced. During training of the neural network, the Adam optimizer and cosine annealing learning rate scheduling are applied to jointly minimize the normalized mean square error and total loss of learned dictionary correlations. Experimental results demonstrate that the proposed method, at a representative signal-to-noise ratio of 20 dB, achieves normalized mean square errors of −24.5 dB. The root mean square errors for angle and distance estimation were 0.50° and 0.18 m, respectively, achieving superior performance relative to heuristic algorithms and deep learning-based algorithms. Beyond its strong robustness to different grid granularities and spatial distances, the proposed method also achieves competitive pilot overhead compared with other approaches. Full article

A capillary-driven microfluidic electrical screening platform was developed using silver nanoparticle (AgNP)-decorated laser-patterned villous microstructures on a glass substrate for the analysis of H3N2-infected A549 cells. The device integrated nanosecond laser patterning, AgNP conductive thin-film formation, passive capillary transport, and direct electrical readout within a single microfluidic sensing structure. Villous-like arrays were fabricated using a 1064 nm IR pulsed laser at a fluence of 4.35 J/cm², with a repetition rate of 300 kHz, pulse overlap of 96.7% and scanning speed of 500 mm/s. The fabricated structures exhibited a diameter of 60 μm, height of 80 μm and interpillar pitches ranging from 30 to 90 μm. After AgNP deposition, the surface showed a dominant Ag content of 59.2%, confirming successful formation of conductive microstructured electrodes. The 30 μm pitch structure produced the highest current response of 22 μA at 1 V and the highest ΔI_norm of 0.053 after introduction of H3N2-infected A549 samples. Wettability and capillary transport were tunable by pitch, with contact angles (CAs) decreasing from 140° to 30° and flow velocities decreasing from 0.1 mm/s to 0.03 mm/s. Formalin-fixed H3N2-infected A549 cells were electrically distinguished from non-infected A549 controls over 10¹–10⁶ PFU/μL, with detectable responses down to 10¹ PFU/μL. These results demonstrate a label-free, self-driven, and fabrication-oriented microfluidic strategy for electrical screening of virus-associated cellular samples. Full article

Gold pillar-based plasmonic metasurfaces provide a robust platform for optical sensing owing to their strong localized surface plasmon resonances and tunable near-field distributions. In this work, we present a numerical investigation of a gold-pillar metasurface optimized specifically for high-performance refractive-index sensing. A comprehensive parametric optimization of the metasurface was carried out, including analyses of the inter-pillar gap, the thickness of the underlying gold film, and the optical response under varying angles of incidence. Numerical analysis demonstrates that the engineered plasmonic pillar array achieves a sensitivity of 500 nm/RIU and a Figure of Merit (FOM) of 79. These results demonstrate the potential of the proposed plasmonic metasurface as an optimized platform for high-performance refractive-index sensing, providing practical design guidelines for future chemical and biological sensing applications. Full article

Flexible sensors are important for wearable health monitoring, strain detection, and temperature sensing because of their mechanical flexibility and functional versatility. Here, a femtosecond laser direct scanning method was used to fabricate porous laser-induced graphene (LIG) and further modify it with platinum nanoparticles (PtNPs), forming Pt/LIG. This mask-free and rapid process enables simultaneous patterning and functionalization of flexible sensors. The introduction of PtNPs improves the electron transport and surface adsorption properties of LIG. As a result, the sheet resistance of Pt/LIG is reduced to 2.41 Ω/sq, enhancing electrical conductivity and suitability for sensing applications. Based on this method, highly sensitive strain and temperature sensors were fabricated. The Pt/LIG strain sensor shows a ΔR/R₀ of 1141.8 at a bending angle of 90°, about 213% higher than that of pristine LIG, with fast response and recovery times of 36 and 56 ms, respectively. The temperature sensitivity also improved by about 650%, with a temperature coefficient of resistance of 0.240%/°C, compared with −0.032%/°C for pristine LIG. Overall, this work provides a fast and precise strategy for fabricating nanoparticle–graphene composites for flexible electronics, wearable health monitoring, and environmental sensing. Full article

Reliable state estimation is essential for the secure and efficient operation of sustainable energy systems, especially under the increasing integration of renewable energy, distributed resources, and heterogeneous sensing devices. However, in practical power systems, SCADA, PMU, and AMI measurements often have different sampling rates, accuracies, communication delays, and availability levels, which makes reliable data completion and multi-source fusion difficult. This paper focuses on the state estimation problem of renewable-integrated distribution networks under multi-source heterogeneous measurement conditions. In such distribution networks, the increasing penetration of distributed renewable energy resources and the joint deployment of multiple measurement devices, including SCADA, PMU, and AMI, may lead to incomplete measurements, asynchronous sampling, differences in measurement accuracy, and reduced system observability. To address these issues, this paper proposes a model-based digital twin reference-guided physics-constrained DDPM framework to improve the quality of missing-measurement completion and the reliability of state estimation in distribution-network scenarios. A four-layer simulation-oriented cyber–physical framework is first constructed to integrate physical sensing, model-based digital twin reference mapping, AI-based measurement completion, and state estimation feedback. Within this framework, a physics-constrained self-supervised denoising diffusion probabilistic model is developed to recover missing measurements by combining observed data, digital twin reference measurements, real-time topology information, and power system operational constraints. The completed pseudo-measurements and physical measurements are then fused through a credibility-aware weighting strategy that considers timeliness, data integrity, measurement accuracy, and virtual–real consistency verification under simulation settings. Simulation results on the IEEE 14-bus system show that the proposed method improves pseudo-measurement completion and supports more reliable voltage magnitude and phase angle estimation under different measurement configurations. Under the tested simulation settings and multi-source measurement configurations, the results indicate that the proposed method can improve pseudo-measurement completion and support more reliable voltage magnitude and phase angle estimation. However, its performance under frequent topology switching, high missing-data ratios, and complex abnormal data conditions remains to be further evaluated. Full article

Archaeological feature detection at complex, mixed-environment sites requires accurate, efficient methods for identifying subtle morphological signatures. This study presents a semi-automated remote sensing pipeline for the detection and delineation of archaeological earthworks at Cahokia Mounds (Illinois, USA), a major Mississippian urban center and UNESCO World Heritage Site. Three LiDAR datasets, two collected via UAV-mounted sensors and one from a piloted aircraft survey, were processed into Digital Terrain Models and transformed into Local Relief Models (LRM). K-means clustering was applied to segment the LRMs into feature classes, followed by contour bounding using the OpenCV library to outline mounds and borrow pits. Additionally, SAR-derived Local Incidence Angle (LIA) rasters from PALSAR-3 and Sentinel-1 were processed through angular deviation mapping to identify slope anomalies associated with archaeological features. Results across all five datasets demonstrate the complementary strengths of LiDAR and SAR: LiDAR excels at resolving elevation-defined features such as mound footprints, while LIA captures directional slope behavior that highlights mound edges, borrow pit rims, and linear features such as causeways. Comparative analysis of LiDAR acquisition frequencies reveals minimal differences in archaeological feature recovery between pulse settings, suggesting that sensor platform choice matters more than power-density tradeoffs for this application. Despite the need for human review to filter modern disturbances and natural false positives, the integrated workflow meaningfully accelerates prospection and reduces interpretive subjectivity. The methods are scalable, site-invariant, and work with open-access data, making them applicable to archaeological landscapes worldwide. Full article