Search Results (9,440)

Deflection is a crucial indicator for structural safety assessment and maintenance of engineering structures. Traditional deflection inspection methods are confronted with the difficulty in selecting reference points, and therefore these methods are usually applied in short-term monitoring of structures. In this context, a novel strategy for automatic deflection inspection of beam-like composite structures which overcomes the difficulty in selecting reference points is put forward in this article. First, deflection assessment of composite structures using long-gauge fiber optic sensing was theoretically established. The relationship between vertical displacement and monitored average strain is irrelevant to external loads. The approach is applicable to both linear and nonlinear stages of structures, and deflection distribution along the structures can be estimated. Second, a four-point loading experiment on a wood–concrete composite beam which was installed with long-gauge fiber optic sensors was performed to verify the reliability of the deflection inspection method. Deflection was estimated under three conditions: (1) without considering composite action; (2) considering composite action but neglecting interface slip; and (3) considering both composite action and interface slip. Meanwhile, displacement meters were also installed to verify the calculated results. Experimental results indicate that the presented strategy has high precision. Hence, the presented method serves as an innovative option for assessing composite structures in both the short and long term. Full article

Wavefront sensing (WFS) is fundamental to adaptive optics, astronomical observation, biological microscopy, and free-space optical communications. However, conventional approaches—including Shack–Hartmann sensors, shearing interferometers, and transport of intensity equation-based methods—are inherently limited by trade-offs among spatial sampling density, angular dynamic range, and device compactness and have rarely been extended to the mid-infrared range. Here, we propose an on-chip mid-infrared wavefront sensing scheme operating based on vectorial photocurrent manipulation and analyze the properties of the proposed device through finite-element simulations. The proposed device comprises a hexagonal array of antenna-integrated graphene pixels, each equipped with three contacts and a microlens. Based on the antenna-induced vectorial photocurrent manipulation, angle-dependent absorption is translated into photocurrent signals, potentially enabling unambiguous recovery of both the elevation and azimuth angles of the incident light over an effective angular dynamic range of ±28°. The hexagonal layout provides a high spatial sampling density of 11,547 mm⁻². Southwell algorithm-based wavefront reconstruction and numerical simulations yield faithful recovery of parabolic, conical, and quadrangular pyramidal wavefronts. In addition, simulation results indicate that this approach can enable high-fidelity reconstruction of both the phase and intensity distributions of an object based on angular-spectrum diffraction theory. Overall, this work theoretically demonstrates a new route toward high-density wavefront measurement and complex light field imaging in the mid-infrared range without a conventional imaging lens. Full article

Human kinematic analysis is an increasingly important tool in biomechanics, human performance, and wearable sensing research. Many emerging sensing modalities utilize custom sensors requiring accurate temporal alignment with ground-truth biomechanical movement data. Optical motion capture systems provide high-fidelity kinematic measurements but operate as closed, self-contained systems, making time synchronization with external sensor data non-trivial, particularly in wireless and mobile contexts. This work presents a wireless analog interface system built using commercially available components that enables alignment between analog sensor data (e.g., from custom wearables and Internet-of-Things devices) and a commercial motion capture system. The proposed architecture consists of a wearable data acquisition node and a receiver node interfaced directly with an optical motion capture system, allowing synchronized recording of analog sensor signals alongside kinematic data. Notably, the system reconstructs signals into the commercial hardware interface rather than relying on triggers or sync outputs, resulting in a single data file containing kinematics and sensor readings. Benchtop testing demonstrated a mean end-to-end frame delay of ~6 ms, with 95% of the sample exhibiting delay within 15 ms. Accounting for the typical offset, this leaves a standard deviation of 4 ms, within one motion capture frame of the true timestamp (at 100 Hz). Voltage reconstruction accuracy was within 30 mV across the tested conditions, with gain compression below 2.7%. Adjacent channel crosstalk remained below −83 dB across all test conditions. The use of commercial off-the-shelf components supports replication and adaptation by other research groups and integration with different optical motion capture systems. Full article

Suspended ceiling systems are among the most seismically vulnerable non-structural components in buildings, posing significant life-safety risks and economic losses, yet understanding their full-field kinematic behavior under seismic loading remains a major experimental challenge. Conventional contact sensors offer limited spatial coverage and can alter the dynamic properties of lightweight panels due to mass loading. In contrast, non-contact optical alternatives are rarely feasible in shake-table environments due to restricted viewing angles, extensive areal coverage requirements, and the risk of equipment damage from falling panels. This study proposes an end-to-end three-dimensional displacement measurement framework for large-scale shake-table testing of suspended ceiling systems, employing consumer-grade cameras with purpose-built tools that cover the complete experimental workflow, including motion-based video trimming, semi-automated calibration, a robust multi-stage image-tracking pipeline that maintains trajectory continuity under extreme inter-frame displacements, and a ceiling system motion visualization and analysis tool. The framework was validated through a full-scale shake-table experiment continuously tracking 324 spatial nodes across 81 ceiling panels, achieving an RMSE below 3 mm in all spatial directions and exact peak-frequency agreement in 9 out of 10 test cases. A parallel processing architecture reduced total processing time from over 27 h to under 10 min without GPU acceleration, and six-degree-of-freedom rigid-body analysis resolved the complete panel failure sequence from constrained oscillation through multi-axis rotation to gravitational free fall, a level of kinematic detail unattainable with conventional instrumentation. This framework establishes a practical, scalable foundation for full-field seismic performance assessment of non-structural systems where conventional instrumentation is physically or logistically infeasible. Full article

This study presents an experimental and numerical investigation on the impact of embedded fiber optic sensors on the mechanical properties, like tensile, compression, bending and compression-after-impact properties, and sensing performances of intelligent composites. The influence by different volume fractions of embedded fiber optics on the mechanical properties was revealed. Combined with finite element simulations, the effect of embedded sensors on the basic mechanical properties of composite materials was obtained. The sensing performance of the embedded fiber Bragg grating (FBG) sensors was validated through comparison with conventional strain gauges. Full article

This study demonstrates single-channel fiber Bragg grating (FBG) sensing for relative vibration-state monitoring of a motor–support system under angle-dependent boundary conditions. A packaged FBG accelerometer-type sensing unit was mounted on the motor–support structure, and the reflected Bragg wavelength was recorded as a one-dimensional optical vibration response. Because the sensor was installed away from the rotating shaft, the measured wavelength fluctuation was interpreted as a coupled vibration-sensitive response of the motor, fixture, sensor package, bonding condition, and changing boundary state, rather than as a calibrated shaft speed or absolute acceleration signal. Adaptive variational mode decomposition (AVMD) was applied to track the time-varying narrowband spectral-response trajectory of the Bragg-wavelength signal. In parallel, raw wavelength windows were supplied to LSTM, 1D-CNN, and CNN–LSTM autoencoders to evaluate waveform departures from learned nominal fixed-angle behavior. The fixed-angle results showed stable but distinguishable optical vibration responses under different boundary states, whereas the dynamic angle-transition records produced local trajectory changes and alarm-candidate intervals. Baseline and autoencoder comparisons further clarified the trade-off between transition coverage and false-alarm tendency. The RMS threshold baseline was more sensitive to transition-related amplitude changes but produced more false alarms, whereas the CNN–LSTM autoencoder provided the most selective response among the tested autoencoder branches. The results are interpreted as task-specific evidence for relative vibration-state transition monitoring rather than as general motor fault diagnosis. Overall, the framework demonstrates a compact FBG-based route for relative vibration-state transition monitoring when speed references, dense sensor layouts, and labeled fault data are unavailable. Full article

Plastic pollution is a problem for many municipalities, water authorities, and industries, including transportation, energy, agriculture, fisheries, real estate, tourism, hospitality, insurance, and healthcare. Efforts to understand and mitigate plastic pollution would benefit from a dedicated map satisfying basic criteria including traceability, scalability, spatio-temporal resolution, and data flexibility. This article details and demonstrates how several existing pollution maps satisfy these criteria and makes recommendations on their use for specific activities, including temporal monitoring, root-cause analysis (RCA), cleanups, and tourism guides. Advantages of using plastic density rather than piecewise logs as the primary data format are highlighted, in particular feasible memory requirements and access to cloud data. Environmental plastic mapping by passive optical sensors, which offer the potential of comprehensive qualified data, is also surveyed, including demonstration of an original shortwave infrared (SWIR) polarization imager, and dynamic plastic pollution monitoring is demonstrated through the application-programming interface (API) of the Google Maps platform utilizing both sensor and published survey data. Full article

Two-dimensional (2D) materials have emerged as a key platform for next-generation electronics due to their atomic thickness and tunable properties. Iron disulfide (FeS₂), known as pyrite, with a bandgap of ~0.95 eV, is suitable for solar energy applications. However, its performance is limited by defects in bulk crystals. Reducing FeS₂ to a single layer eliminates bulk defects and enables strain engineering of the bandgap. In this study, First-principles density functional theory (DFT) calculations are performed using the CASTEP code and the PBEsol functional to examine the structural, electronic, and optical properties of a distorted 1T′-phase FeS₂ monolayer. Full geometry optimization yields lattice parameters a′ = 17.594 Å, b′ = 3.20231 Å, c′ = 5.28091 Å, and Fe–S bond angles of ~75.8° and ~98.2°, confirming symmetry-breaking distortion. The monolayer is dynamically stable, showing no imaginary modes in the phonon dispersion, and remains structurally intact up to 1000 K in molecular dynamics simulations. The unstrained system has an indirect bandgap of 0.70 eV, with the valence band maximum at the Γ point (dominated by S-p states) and conduction band minimum near the X point (Fe-d states). Under mechanical strain (±4%), the bandgap decreases significantly: from 0.70 eV to 0.44 eV under +4% tensile strain along the y-axis, and to 0.53 eV under −4% compressive strain. Biaxial strain causes weaker modulation, reducing the gap to 0.66 eV (+4%) and 0.62 eV (−4%). Optical absorption exceeds 10⁴ cm⁻¹ for photon energies above the bandgap, with tensile strain causing redshifts and compressive strain inducing blueshifts. These findings demonstrate that 2D FeS₂ is mechanically robust, electronically tunable, and optically active, making it a promising candidate material for flexible strain sensors and photovoltaic devices. This work is intended to motivate and inform future synthesis efforts. Full article

This paper proposes and experimentally demonstrates a fiber ring laser (FRL) salinity sensing system based on a Sagnac loop (SL) formed by a tapered polarization-maintaining fiber (TPMF). The operating principle is that salinity modulates the birefringence of the polarization-maintaining fiber (PMF), causing a shift in the interference wavelength of the SL transmission spectrum, while the FRL narrows the optical spectrum and enhances the signal-to-noise ratio (SNR). In the experiment, the SL consists of a 20-cm-long PMF with a tapered waist diameter of 10.86 μm. Over the salinity range of 0‰ to 30‰, the sensitivity of the laser-based sensing system is 97 pm/‰, which agrees well with the 93 pm/‰ sensitivity obtained using a broadband light source (BBS), and the salinity exhibits a good linear relationship with the wavelength shift, with a coefficient of determination (R²) of 0.997. Meanwhile, the ring laser cavity improves the SNR of the sensing system from 22 dB to approximately 54 dB, and compresses the 3-dB bandwidth from 1.75 nm to 0.06 nm. Further adopting the support vector regression (SVR) algorithm for linear regression modeling of the spectral data, the results show that the mean absolute error (MAE) decreases from 0.50‰ to 0.04‰, the root mean square error (RMSE) decreases from 0.54‰ to 0.11‰, and R² reaches as high as 0.99988. To the best of our knowledge, this is the first work that combines salinity laser sensing with an artificial intelligence algorithm. The proposed sensor leverages the narrow linewidth and high SNR advantages of the FRL together with the high-precision linear fitting capability of the SVR algorithm, achieving significantly improved accuracy for salinity measurement compared to conventional spectral demodulation. Full article

Lithium niobate on insulator (LNOI) has emerged as a promising platform for compact, low-loss phase modulators. The extant LNOI studies evaluate device performance almost exclusively through the Pockels effect, treating piezoelectric–photoelastic strain and thermo-optic drift as decoupled channels. Crucially, both mechanisms directly perturb the phase bias of a fiber-optic gyroscope (FOG), rendering them indispensable in sensing-oriented design. This work establishes a unified multiphysics model of an X-cut TFLN ridge phase modulator that self-consistently couples the electro-optic, piezoelectric–photoelastic, thermo-optic, and pyroelectric channels. The contributions of the four mechanisms are quantitatively decomposed under realistic FOG operating conditions, and the slab thickness, ridge-top width, and electrode gap are systematically optimized to balance modulation efficiency against environmental robustness. The co-optimization of the ridge geometry and electrode gap design maintains the EO overlap factor near 0.55, while reducing the half-wave voltage requirement. This results in a half-wave voltage length of V_πL = 1.65 V·cm at a 4.4 μm electrode gap. The optimized geometry and electrode gap (4.4 μm) are essentially temperature-independent: extracted from the Pockels modulation slope, V_πL remains stable at ≈1.65 V·cm (push–pull single-pass; within ~0.3%) across 25~85 °C. Furthermore, an externally imposed substrate temperature rise of 60 K (the upper end of the 25~85 °C FOG operating range) induces a mode-field-weighted thermal residual corresponding to approximately 27% of the Pockels modulation depth at an applied voltage of 5 V. The present study demonstrates that the DC-coupled operation of TFLN sensor-grade modulators is viable across the full FOG temperature range, without dedicated active temperature stabilization, and the residual thermal-bias offset is absorbed by the FOG’s standard closed-loop servo electronics. The results of the study provide quantitative design guidelines for high-performance, environmentally stable TFLN phase modulators in compact FOG systems. Full article

This study presents a multi-indicator remote sensing framework for assessing satellite-derived water-quality-related and trophic-state-related dynamics across four freshwater systems in Türkiye Egirdir Lake, Sapanca Lake, Catalan Dam, and Yuvacik Dam between the baseline (2015–2018) and recent (2023–2025) periods. Rather than providing a regulatory or use-specific satellite-based assessment of water-quality-related indicators, the study evaluates optically and thermally detectable surface water indicators derived from Sentinel-2 MSI and Landsat 8/9 imagery processed in Google Earth Engine. The Normalized Difference Chlorophyll Index (NDCI), the Normalized Difference Turbidity Index (NDTI), and land surface temperature (LST, applied to water surfaces) were used to detect change patterns through period-mean difference mapping (Δ-mask) and interannual time series analysis. Results reveal distinct spatial and temporal dynamics broadly consistent with the interplay of climatic, hydrological, and anthropogenic drivers. In the southern Mediterranean systems, positive ΔNDCI anomalies in littoral and inflow zones were associated with increasing summer LST, with Egirdir Lake exhibiting a statistically significant warming trend of +0.170 °C yr⁻¹ (Mann–Kendall τ = 0.53, p = 0.029), interpreted cautiously as a physically plausible signal consistent with regional climate trends, suggesting elevated thermally mediated eutrophication-related optical risk. In the northern Marmara systems, satellite-observed patterns were more strongly associated with anthropogenic nutrient loading and morphological constraints, with turbidity-related optical increases concentrated in western and marginal zones despite relatively stable thermal conditions. As concurrent in situ measurements were unavailable, cross-sensor consistency checks and literature-based benchmarking were applied as alternative validation strategies. Across all four systems, positive ΔNDCI anomalies were systematically concentrated in shallow marginal and inflow zones, while ΔNDTI patterns varied by system, underscoring the role of littoral dynamics as early indicators of optically detectable water-quality deterioration and trophic-state-related change. The proposed framework offers a scalable, cost-effective approach for freshwater quality surveillance in data-scarce environments and provides direct support for integrated water resource management under Türkiye’s National Water Plan (2026–2036). Full article

This work presents an enhanced photomechanical optical sensor inspired by our previously reported bio-inspired uncooled infrared detector. Performance improvement is achieved by strengthening the interfacial bond between the photothermal dendrite—polydopamine nanoparticle (PDA NP)/polydimethylsiloxane (PDMS) composite—and the piezoresistive laser-induced nanocarbon film, with a flexible PDMS substrate that provides both thermal insulation and mechanical stability. The resulting sensor exhibits a responsivity of 51.6 W⁻¹ under 808 nm irradiation, an order-of-magnitude enhancement over the unmodified device. Wavelength-dependent characterization (455–1550 nm) shows responsivity decreasing from 93.1 W⁻¹ at 455 nm to 14.4 W⁻¹ at 1550 nm, with response times on the order of seconds across this range. Extending this trend into the longer-wavelength region of blackbody radiation, the mechanism transitions to a predominantly bolometric mode. The device also demonstrates stable detection of several hundred microwatts and robust durability at 455 nm. These results validate interface engineering strategy as a viable pathway toward high-performance uncooled optical detection, advancing bio-inspired detectors from functional mimicry toward an application-ready platform. These findings confirm PDA NPs as effective photothermal converters primarily at shorter wavelengths, while the wavelength-dependent response suggests future tailoring of spectral sensitivity using long-wavelength-absorbing materials. Full article

Although optical fiber-based surface plasmon resonance (SPR) sensors have revolutionized real-time, label-free biosensing, conventional designs suffer from limited multi-analyte detection capabilities. This study utilizes the novel Pi (π)-configured dual SPR optical fiber sensor with two opposing side-polished surfaces, enabling plasmonic excitation for simultaneous multi-analyte detection. The proposed sensor leverages asymmetric metallic thin films such as Ag, Au, Cu, and hybrid configurations (metal + TiO₂) to generate two distinct resonance peaks, significantly enhancing detection versatility. Numerical simulations using the finite element method in COMSOL Multiphysics v6.3 demonstrate that the π-configuration achieves dual resonance dips at 982 nm and 1276 nm for Ag and Ag–TiO₂ films, 1040 nm and 1317 nm for Au and Au–TiO₂ films, and 977 nm and 1249 nm for Cu and Cu–TiO₂ films, respectively, for an analyte refractive index of 1.42. A peak spectral separation >125 nm was achieved for all the sensors for a refractive index range of 1.37–1.42, ensuring that the two dips are resolvable since the change in SPR wavelength is greater than or equal to the full width at half maximum, preserving dual-analyte capability and minimizing potential crosstalk. The results indicate that the π-configured dual SPR sensor utilizing silver and silver–TiO₂ sensing layers had the highest wavelength sensitivity of 12,600 nmRIU⁻¹ and 20,000 nmRIU⁻¹, respectively, slightly outperforming its gold and copper counterpart. The optimized metallic and hybrid nanostructured films ensure dual distinct peaks with high sensitivity, while maximizing refractive index resolution. This work presents the design of a π-configured SPR-based optical fiber sensor utilizing dielectric and multi-metallic thin films, thereby offering a breakthrough in multiplexed biosensing for applications in medical diagnostics, environmental monitoring, and chemical detection. Full article

Line confocal sensors provide non-contact, high-resolution, and high-efficiency measurement and can be integrated into optical measurement systems such as Photon for three-dimensional topography measurement of complex surfaces. However, installation-induced tilt-pose errors of the sensor can couple height information with lateral position, thereby reducing the accuracy of profile reconstruction. To address this issue, this paper proposes a calibration-block-based tilt-pose error identification and compensation method for line confocal sensors. Using the known geometric features of the calibration block, the proposed method establishes a mapping relationship between sensor tilt-pose errors and measured profile distortion. Sensitivity analysis is performed to identify the dominant error components, and the tilt-pose errors are estimated in a single identification process, enabling quantitative compensation of the measured point cloud. Experimental results show that, after calibration and compensation, the maximum Z-direction height difference in the overlapping profile region of the calibration block is reduced from 12.782 μm to 0.307 μm. The proposed method requires no complex external alignment devices and provides an effective approach for high-precision integrated applications of line confocal sensors. Full article

Snow cover fraction (SCF) records derived from optical satellite sensors such as AVHRR are systematically interrupted by cloud contamination and polar night conditions, leaving large spatiotemporal data gaps that limit their utility for climate and hydrological applications. This study presents a U-Net–based deep learning framework for reconstructing missing SCF values in Scandinavia over a 15-year period (2000–2014), using the ESA CCI L3C SCFV AVHRR v4.0 product as both partial input and training target. The model integrates physically meaningful auxiliary predictors (snow water equivalent (SWE), near-surface air temperature, elevation, and land cover) harmonized to a common 0.05° grid, enabling reconstruction in the complete absence of concurrent optical observations. Trained on a single year with extensive synthetic masking (91.5% of valid SCF pixels withheld), the U-Net achieves an R² of 0.9342 and RMSE of 0.1127, outperforming spatial interpolation, a SWE-based physical baseline, and pixel-wise machine learning baselines. Feature importance analysis confirms that SWE and temperature dominate predictive skill, with the observed SCF input contributing negligibly. Independent validation against ground station observations yields 86.7% binary classification accuracy and an F1 score of 88.0%, comparable to the 87.8% accuracy of the original satellite retrievals, demonstrating the viability of deep learning–based gap-filling for producing continuous SCF records under cloud cover and polar night. Full article