Search Results (9,099)

This work presents a signal processing and reconstruction system developed for a flexible optical pressure 2D mapping sensor. The sensor consists of a two-dimensional grid of polyurethane optical fibers (PU-OFs) embedded in polydimethylsiloxane (PDMS), which acts as the input device for acquiring light intensity changes caused by external surface-applied pressure. In this study, we propose a system to process these signals through an inverse model based on the Moore–Penrose pseudoinverse for spatial localization, along with a point-specific pressure estimation model to infer the magnitude of the applied force, which is then used to generate quantitative pressure maps. Experimental results show the system’s overall performance, robustness, and repeatability across multiple pressure levels and locations. In most cases, localization errors remain below 5 mm, while pressure estimation errors are around 5 mmHg when the pressure is correctly localized. Performance metrics, such as recall, specificity, and precision, support the system’s ability to detect, localize, and reconstruct pressure events with consistent reliability. These results establish the viability of the proposed methodology for potential integration into low-cost and flexible optical fiber-based 2D pressure monitoring systems for biomedical applications. Full article

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) offers the flexibility to position sensors closer to the scalp, which improves the signal-to-noise ratio compared to conventional superconducting quantum interference device (SQUID) systems. However, the spatial resolution of OPM-MEG critically depends on sensor placement, especially when the number of sensors is limited. In this study, we present a methodology for optimizing OPM-MEG sensor layouts using each subject’s anatomical information derived from individual magnetic resonance imaging (MRI). We generated realistic forward models from reconstructed head surfaces and simulated magnetic fields produced by equivalent current dipoles (ECDs). We compared multiple simulation strategies, including ECDs randomly distributed across the cortical surface and ECDs constrained to regions of interest. For each simulated magnetic field map (MFM) database, we applied the sequential selection algorithm (SSA) to identify sensor positions that maximized information capture. Unlike previous approaches relying on large measurement databases, this simulation-driven strategy eliminates the need for extensive pre-existing recordings. We benchmarked the performance of the personalized layouts using OPM-MEG datasets of auditory evoked fields (AEFs) derived from real whole-head SQUID-MEG measurements. Our results show that simulation-based SSA optimization improves the coverage of cortical regions of interest, reduces the number of sensors required for accurate source reconstruction, and yields sensor configurations that perform comparably to layouts optimized using measured data. To evaluate the quality of estimated MFMs, we applied metrics such as the correlation coefficient (CC), root-mean-square error, and relative error. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95) capture most of the information contained in full-head MFMs. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles and found that localization errors were < 5 mm. The results further indicate that SSA performance is insensitive to individualized head geometry, supporting the feasibility of using representative anatomical models and highlighting the potential of this approach for clinical OPM-MEG applications. Full article

Carbon dots (CDs) have enormous potential in optical sensing applications due to their remarkable physicochemical properties. Benefiting from high specific surface area, rich active sites, bright photoluminescence, high photostability, and biocompatibility, CDs have been widely used as functional layers in optical fiber sensors, resulting in notable improvements in sensitivity, response speed, and environmental stability. This review describes recent advances in CD-integrated optical fiber sensors, with a focus on CD synthesis techniques and their integration with optical fibers for the sensing of diverse analytes, including heavy metal ions, biomarkers, and dyes. CD-integrated fiber sensors exhibit significantly enhanced detection performance in terms of sensitivity, selectivity, repeatability, response time, and recovery time, compared with their CD-free counterparts. Finally, current challenges and future perspectives are discussed. This review aims to provide valuable insights for the design and development of novel CD-integrated optical fiber platforms for sensing chemically and biologically relevant analytes. Full article

We present a reference-free computational wavefront sensor based on binary amplitude modulation and phase retrieval. The method employs a Digital Micromirror Device as a programmable amplitude modulator and reconstructs the complex optical field from multiple far-field intensity measurements using the Reweighted Amplitude Flow algorithm with Optimal Spectral Initialization. Unlike classical pupil-plane wavefront sensors, the proposed architecture contains no wavelength-specific optical elements, enabling straightforward adaptation across a broad spectral range. The achievable spatial resolution of the reconstructed wavefront scales directly with the modulator resolution. We experimentally demonstrate wavefront reconstruction at 650 nm and at 2116 nm, a wavelength regime where commercial wavefront sensors are scarce. At 650 nm, the reconstructed wavefront is validated against a commercial lateral shearing interferometer, and the sensor is further integrated into a closed-loop adaptive optics system using a deformable mirror. The proposed approach is particularly suited for applications requiring high spatial resolution and large dynamic range in slowly varying or quasi-static laser fields, where computational reconstruction speed is not a primary concern. Full article

Sea surface temperature (SST) is widely used to characterize marine productivity, environmental pollution, and climate variability, and is commonly derived from thermal infrared measurements obtained by optical satellite sensors. However, accurately retrieving large-scale SSTs remains challenging due to the complexity of air–sea coupling processes and the difficulty of accurately obtaining key intermediate parameters. This study proposes a day–night-differentiated SST retrieval method with emissivity correction rather than treating it as a fixed value. Specifically, radiance characteristics from the mid-infrared band are integrated alongside those from thermal infrared bands. The retrieved SSTs are then validated against the MODIS SST product and in situ measurements. The results demonstrate strong consistency between the retrieved SST and the MODIS SST product, with overall root mean square errors (RMSEs) of 0.66 K and 0.82 K for daytime and nighttime, respectively. In winter the RMSEs improve to 0.37 K (day) and 0.42 K (night). In situ validation against Argo measurements in 2019 shows that the RMSEs of the retrieved SSTs are approximately 0.26 K for both day and night. This confirms the efficacy of the proposed SST retrieval approach, providing a feasible solution for high-precision SST retrieval in high-latitude regions with large view zenith angles. Full article

Measuring water motion is essential for oceanography, coastal engineering, and marine environmental monitoring. A wide range of sensing technologies is used to quantify water velocity, wave motion, and flow dynamics, each suited to specific spatial and temporal scales. This paper presents a comprehensive review of modern sensor technologies for marine flow measurement, covering mechanical, electromagnetic, pressure-based, acoustic, optical, MEMS-based, inertial, Lagrangian, and remote-sensing approaches. The operating principles, strengths, and limitations of each technology are examined alongside their suitability for different environments and deployment platforms, including moorings, buoys, vessels, autonomous underwater vehicles, and drifters. Special attention is given to rapidly advancing fields such as MEMS flow sensors, multi-sensor fusion, and hybrid systems that combine inertial, acoustic, and optical data. Applications range from high-resolution turbulence measurements to large-scale current mapping and wave characterization. Remaining challenges include biofouling, performance degradation in energetic shallow waters, uncertainties in indirect velocity estimation, and long-term calibration stability. By synthesizing the state of the art across sensing modalities, this review provides a unified perspective on current technological capabilities and identifies key trends shaping the future of marine flow measurement. Full article

A photoelectric tracking system is a typical bearing-only target tracking system that faces significant challenges arising from measurement origin uncertainty due to clutter and the discrepancy between continuous-time target dynamics and discrete-time optical sampling, as well as the inherent nonlinearity of bearing-only tracking. This paper addresses these issues by proposing a novel distributed probabilistic data association feedback particle filter (DPDA-FPF) framework. To resolve the tracking ambiguity at the local level, we extend the feedback particle filter to a continuous-discrete setting integrated with probabilistic data association. Subsequently, the local state estimates and covariances from spatially separated tracking systems are transmitted to a fusion center and integrated using an optimal linear covariance-weighted fusion rule to improve global observability and mitigate biases of individual systems. Numerical simulations in a 3D scenario with moderate clutter density demonstrate that while individual sensor tracks suffer from fluctuations, the proposed fused estimate achieves substantially lower root mean square errors in both position and velocity. The results validate the efficiency of the proposed architecture as a robust solution for photoelectric tracking applications. Full article

We present a Genetic Algorithm (GA)-based inverse design framework for creating a single-layer, fabrication-compatible dielectric nano-patterned surface that enables efficient color routing in both transmissive and reflective optical systems. Unlike traditional multilayer or absorption-based color filters, the proposed structure employs a fabrication-compatible architecture that spatially routes red, green, and blue light into designated output channels, significantly enhancing light utilization and color fidelity. The design process integrates a GA with full-wave finite-difference time-domain (FDTD) simulations to optimize the structural pillar height distribution, using a figure of merit that simultaneously maximizes optical efficiency and minimizes spectral crosstalk. For CMOS image sensor-scale designs, the nano-patterned surface achieved peak optical efficiencies of 76%, 72%, and 78% for blue, green, and red channels, respectively, with an average efficiency of 75.5%. Parametric studies further revealed the dependence of performance on pillar geometry, refractive index, and unit cell scaling, providing practical design insights for scalable fabrication using nanoimprint or grayscale lithography. Extending the approach to reflective displays, we demonstrate tunable-mirror-based architectures that emulate electrophoretic microcapsules, achieving efficient color reflection and an expanded color gamut beyond the sRGB standard. This single-layer, inverse-designed nano-patterned surface offers a high-performance and fabrication-ready solution for compact, energy-efficient imaging and display technologies. Full article

Blood pressure (BP) monitoring is crucial for identifying high BP (hypertension) and is an important aspect of patient care. However, traditional cuff-based methods for BP monitoring are unsuitable for continuous monitoring and can cause discomfort to patients. This survey critically examines the emerging field of cuffless BP monitoring, highlighting advances beyond traditional cuff-based methods. Technologies such as radar, optical, acoustic, and capacitive sensors offer the potential for continuous, non-invasive BP estimation, enabling applications in remote health monitoring and ambient clinical intelligence. We introduce a unifying taxonomy covering sensing modalities, physiological measurement principles, signal processing techniques, and translational challenges. Emphasis is placed on methods that eliminate subject-specific calibration, overcome motion artifacts, and satisfy international validation standards. The review also analyses Machine Learning (ML) and sensor fusion approaches that enhance predictive accuracy. Despite encouraging results, challenges remain in achieving clinically acceptable accuracy across diverse populations and real-world conditions. This work delineates the current landscape, benchmarks performance against gold standards, and identifies key future directions for scalable, explainable, and regulatory-compliant BP monitoring systems. Full article

As a class of self-organized soft matter systems merging fluidic mobility with long-range molecular order, cholesteric liquid crystals (CLCs) possess immense potential for the development of high-sensitivity, visually tractable flexible sensors. Leveraging their unique helical superstructures and stimuli-responsive photonic bandgaps, CLCs can transduce subtle physical or chemical perturbations into discernible optical signatures, such as Bragg reflection shifts or mesomorphic textural transitions. Nonetheless, the intrinsic fluidity of CLCs often compromises their structural integrity, while conventional one-dimensional (1D) or two-dimensional (2D) confinement geometries exhibit pronounced angular dependence, significantly constraining their detection precision in complex environments. Recently, microfluidic technology has emerged as a pivotal paradigm for achieving sophisticated three-dimensional (3D) spatial confinement of CLCs through the precise manipulation of microscale fluid volumes. This review systematically delineates recent advancements in microfluidics-enabled CLC sensors. Initially, the fundamental self-assembly principles and optical properties of CLCs are introduced, emphasizing the unique advantages of 3D spherical confinement in mitigating angular sensitivity and intensifying interfacial interactions. Subsequently, the primary sensing mechanisms are bifurcated into bulk-driven sensing via pitch modulation and interface-driven sensing via topological configuration transitions. We then detail the microfluidic-based fabrication strategies and engineering protocols for diverse 3D architectures, including monodisperse/multiphase droplets, microcapsules, shells, and Janus structures. Building upon these structural frameworks, current sensing applications in physical (temperature, strain/stress), chemical (volatile organic compounds, ions, pH), and biological (biomarkers, pathogens) detection are evaluated. Lastly, in light of persistent challenges, such as intricate signal interpretation and limited robustness in complex matrices, we propose future research trajectories, encompassing the co-optimization of geometric parameters (size and curvature), artificial intelligence-enhanced automated diagnostics, and multi-field-coupled intelligent integration. This work seeks to provide a comprehensive roadmap for the design of next-generation, high-performance, and portable liquid-state photonic sensing platforms. Full article

This paper presents a cover lens concept for camera modules based on surface acoustic waves (SAW) to mitigate the degradation of physical AI optical sensor field-of-view performance caused by surface contamination. The proposed approach utilizes a single-phase unidirectional transducer (SPUDT) that intentionally breaks left–right symmetry through a geometrically asymmetric electrode array to generate SAW, thereby removing droplet contamination. First, the acoustic streaming induced inside a single sessile droplet by the SAW was visualized, and the dynamic behavior of the droplet upon SAW actuation was observed using a high-speed camera. The internal flow developed into a recirculating vortex structure with directional deflection relative to the SAW propagation direction, indicating a symmetry-broken streaming pattern rather than a purely symmetric circulation. Upon the application of the SAW, the droplet was confirmed to move a total of 7.2 mm along the SAW propagation direction, accompanied by interfacial deformation and oscillation. Next, an analysis of transport trajectories for five sessile droplets dispensed at different y-coordinates ($y_1$–$y_5$) revealed that all droplets were transported along the x-axis regardless of their initial positions. Furthermore, the analysis of transport velocity as a function of droplet viscosity (1 $\mathrm{c}\mathrm{P}$ and 10 $\mathrm{c}\mathrm{P}$) and volume (2$\mathrm{\mu }\mathrm{L}$, 4$\mathrm{\mu }\mathrm{L}$, and 6 $\mathrm{\mu }\mathrm{L}$) demonstrated that the transport velocity gradually increased with driving voltage but decreased as viscosity increased under identical actuation conditions. Finally, the proposed cover lens was applied to an automotive front camera module to verify its effectiveness in improving object recognition performance by removing surface contamination. Based on its simple structure and driving principle, the proposed technology is deemed to be expandable as a surface contamination cleaning technology for various physical AI perception systems, including intelligent security cameras and drone camera lenses. Full article

Investigating the long-term performance of building materials, such as drying shrinkage, moisture expansion, creep, and others, usually requires long-lasting tests with a high number of specimens. Given the initial costs, required data acquisition systems, and the time allocated, conventional sensors like LVDTs become costly for such long-term experimental studies. This article proposes an innovative cost-effective solution combining optical microscopy imaging, 3D printed sliding rulers, and Python-based artificial vision to overcome these limitations. The 3D printed rulers establish a local physical reference frame, while the artificial vision system uses contour detection and point tracking of optical targets to quantify displacements. Unlike continuous monitoring systems, the proposed solution utilises a discontinuous point-tracking approach, allowing a single USB microscope to monitor an unlimited number of specimens while maintaining the possibility for moisture exchange between the material surface and the environment. The system was metrologically validated against a laser interferometer, achieving an expanded instrumental uncertainty of 0.0042 mm (4.2 µm), determined through strict calibration. These results demonstrate that the proposed solution delivers accuracy comparable to conventional sensors but with significantly higher scalability and lower cost, making it highly suitable for extensive long-term experimental programmes. Full article

Severe Dengue fever can cause Dengue Hemorrhagic Fever (DHF), a life-threatening condition characterized by plasma leakage and hemoconcentration. A hematocrit (Hct) rise of ≥20% is a key indicator for medical intervention, but current monitoring is invasive and intermittent. This study aims to determine the optimal design parameters for a non-invasive optical sensor to continuously monitor hemoconcentration. We developed a high-fidelity Monte Carlo model of light transport in a multi-layered skin model, with the epidermis set to a 5% melanin volume fraction (Fitzpatrick type II/III). To ensure signal reliability, simulations were conducted with a high photon count ($1\times {10}^8$ photons), yielding a stochastic (Monte Carlo) signal-to-noise ratio of approximately 36 dB. We simulated diffuse reflectance at four characteristic wavelengths (577 nm, 660 nm, 800 nm—the isosbestic point—, and 940 nm) over source-detector separations of 0.5–8.0 mm. Sensor sensitivity was quantified as the reflectance change for a +25% relative Hct rise (e.g., 42% to 52.5%), mimicking severe hemoconcentration, and its dependence on baseline dermal blood volume fraction (BVF) was investigated. Sensor sensitivity showed a non-linear dependence on BVF, showing a direct correlation with perfusion level, reaching an optimal 6.41% for a robust 5% BVF at 8.0 mm. A dedicated sweep showed that even under low-perfusion shock conditions (1% BVF), the sensor maintains a highly significant sensitivity of 5.71% (also at 8.0 mm), indicating that sensitivity remains high across a physiologically relevant perfusion range. In the analysis, at a robust 5% BVF, the 800 nm wavelength demonstrated superior reliability, with peak sensitivity at 6.41% at 8.0 mm. Visible wavelengths (577 nm and 660 nm) exhibited high theoretical sensitivity, while 940 nm was compromised by water absorption. Based on these findings, a non-invasive optical sensor for hemoconcentration is most effective operating at 800 nm, within the evaluated spectral set, with a source-detector separation of ≥6.0 mm, targeting the deep dermis while minimizing superficial interference. This design provides an optimal balance of tissue penetration, robust sensitivity to Hct changes, and reduced sensitivity to oxygenation-related variability while maintaining signal stability. This work enables the design of a device for continuous monitoring, supporting continuous monitoring of hemoconcentration trends relevant to plasma leakage progression. Full article

Camera calibration is an essential research direction in photonics and computer vision. It achieves the standardization of camera data by using intrinsic and extrinsic parameters. Recently, RGB-D cameras have been an important device by supplementing deep information, and they are commonly divided into three kinds of mechanisms: binocular, structured light, and Time of Flight (ToF). However, the different mechanisms cause calibration methods to be complex and hardly uniform. Lens distortion, parameter loss, and sensor degradation et al. even fail calibration. To address the issues, we propose a camera calibration method based on the Expectation–Maximization (EM) algorithm. A unified model of latent variables is established for the different kinds of cameras. In the EM algorithm, the E-step estimates the hidden intrinsic parameters of cameras, while the M-step learns the distortion parameters of the lens. In addition, the depth values are calculated by the spatial geometric method, and they are calibrated using the least squares method under an optical motion capture system. Experimental results demonstrate that our method can be directly employed in the calibration of monocular and binocular RGB-D cameras, reducing image calibration errors between 0.6 and 1.2% less than least squares, Levenberg–Marquardt, Direct Linear Transform, and Trust Region Reflection. The deep error is reduced by 16 to 19.3 mm. Therefore, our method can effectively improve the performance of different RGB-D cameras. Full article

The rapid and accurate detection of SARS-CoV-2 biomarkers remains a critical requirement for effective outbreak control and decentralized diagnostics. Although RT-PCR is the current gold standard, its reliance on centralized laboratories and long processing times limits its applicability in point-of-care settings. In this context, optical biosensing platforms based on surface plasmon resonance (SPR) offer attractive features, including label-free, real-time, and quantitative detection. This study explores the use of synthetic receptors for the highly sensitive detection of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Specifically, soft molecularly imprinted polymer nanoparticles (nanoMIPs) were employed as synthetic receptors and integrated into a high-sensitivity, portable plasmonic platform based on a D-shaped plastic optical fiber (POF) SPR sensor. The nanoMIPs were selectively imprinted against the RBD, characterized by Dynamic Light Scattering (DLS), Isothermal Titration Calorimetry (ITC), and Scanning Electron Microscopy (SEM) to confirm nanoMIPs size, binding properties, and surface morphology. Next, the nanoMIPs were immobilized onto a gold-coated sensing surface, enabling enhanced specificity, affinity, and signal amplification compared to conventional biological recognition elements. The resulting RBD-SPR-nanoMIPs sensor demonstrated promising analytical performance, exhibiting high selectivity against potentially interfering proteins and an anticipated sensitivity suitable for RBD detection at femtomolar concentrations. The inherent stability of nanoMIPs suggests the potential for reusable SPR sensing platforms, paving the way for next-generation synthetic receptor-based plasmonic biosensors. Full article