Search Results (99)

LiDAR sensors provide reliable physical distance measurements using laser signals, enabling accurate acquisition of 3D information for various optical systems. However, they are costly, require significant weight and space, and their reliability and accuracy degrade under adverse environmental and weather conditions. In contrast, thermal cameras operating in the infrared spectrum can capture stable visual information even in challenging scenarios such as nighttime, low-light, and rain. However, they cannot directly provide the physical 3D depth information that LiDAR offers. To design efficient optical systems, there is a growing need for techniques that transform thermal image data into LiDAR-like depth information. While deep learning models can theoretically learn direct mappings between thermal and LiDAR modalities, the scarcity of acquiring paired thermal–LiDAR datasets and the difficulty of acquiring them make this task challenging. In this paper, we propose a thermal image-to-LiDAR depth transformation framework. Our method leverages large-scale pretrained visual models for depth estimation to generate initial depth predictions from thermal inputs. Since pretrained RGB-based models face a modality gap when applied to thermal data, we introduce a two-stage depth refinement. Stage 1 corrects global scale inconsistencies, and Stage 2 refines local structural details. Experiments on the MS2 dataset demonstrate that the proposed framework consistently improves the initial DepthPro outputs across day, night, and rainy conditions. Both quantitative metrics and qualitative comparisons show that RGB-pretrained depth predictions can provide useful structural cues for thermal depth estimation when their global scale and local structural errors are explicitly refined. Full article

Microplastic monitoring needs methods that operate directly in water with minimal sample handling. Conventional techniques such as infrared and Raman spectroscopy and pyrolysis–GC/MS provide polymer-specific information but require sample preparation and delayed laboratory analysis. We propose an optical sensor concept for real-time, in situ microplastic assessment, based on multispectral pulsed transmission in the visible range using synchronized laser-diode lines and the directly transmitted signal through an active sensor volume. After calibration on particle-free water, each particle event reduces to a water-normalized transmission whose deficit is set by geometrical beam–particle overlap and the wavelength-dependent extinction efficiency. The weak polymer absorption is represented by the Urbach-tail formalism, the refractive-index-related redirection of light by a Fresnel-based, surface- and orientation-averaged probability of direct transmission, and particle size and shape are decoupled through an effective optical length. The coupled nonlinear system is solved for the bounds of the polymer absorption coefficient per candidate geometry. Because each polymer occupies a bounded region in multi-wavelength absorption space fixed by its band gap and structural state, the method can, in principle, separate structural modifications of identical composition, such as low- and high-density polyethylene. This is a sensor concept with a model-based proof of concept, not full environmental validation. Experimental verification on real reference particles is reported separately; the present article establishes the measurement model and inversion scheme that this verification builds on. 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

The growing prevalence of chronic diseases, population aging, and the shift toward preventive and personalized care under Healthcare 5.0 have increased the need for continuous health monitoring beyond clinical settings. While wearable devices enable remote monitoring, their long-term use is often limited by user compliance, comfort issues, battery dependence, and disruption of daily routines. To address these limitations, unobtrusive home-based health monitoring systems have emerged, integrating sensing technologies into domestic environments and everyday objects. This review provides a system-level analysis of unobtrusive health monitoring technologies for smart homes. It examines seven major sensing approaches, including camera-, laser-, radar-, infrared-, mechanical-, bioelectrical-, and optical-based sensors, and their integration into four home environments: living areas, bathrooms, bedrooms, and home offices. For each sensing modality, the operating principles, monitored physiological parameters, representative applications, and key advantages and limitations are discussed. Overall, existing solutions reveal trade-offs among measurement accuracy, robustness in real home conditions, energy autonomy, privacy preservation, and user acceptance. Heart rate and respiratory rate are the most commonly monitored parameters, while multimodal and clinically validated systems remain limited. Although unobtrusive sensing technologies show strong potential for proactive and personalized healthcare, challenges related to accuracy, interoperability, privacy, and cost continue to hinder large-scale adoption. Full article

This study presents a comparative evaluation of multiple-approach optical spectroscopic sensor—Laser-Induced Breakdown Spectroscopy (LIBS), Mid-Infrared Spectroscopic sensing (MIRS), and Hyperspectral Imaging (HSI)-based sensors operating in the Visible–Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) ranges—for the quantitative detection of calcium carbonate (CaCO₃) in pelletized CaCO₃-CaO mixtures. The objective was to assess and compare the sensing performance of these optical sensor platforms for carbonate quantification. Each spectroscopic sensor dataset was processed using chemometric calibration methods, including Partial Least Squares Regression (PLSR), to ensure robust and reproducible quantitative predictions. Although the samples consisted of binary CaCO₃-CaO mixtures, the sensing task focused exclusively on CaCO₃ content. Results indicate that LIBS, MIRS, and HSI-SWIR-based sensing approaches achieved comparable quantitative performance, with LIBS providing the highest prediction accuracy. In contrast, the HSI-VNIR sensor configuration demonstrated lower predictive capability relative to the other optical sensing modalities. These findings highlight the potential and limitations of different optical sensor technologies for carbonate detection in heterogeneous mineral systems. Full article

This article examines hazardous ground irregularities that remain undetectable by the white cane used by visually impaired individuals. Additionally, the development of a multi-beam laser ranging system is described. Integrated into the cane handle, this system is designed to provide comprehensive ground awareness and sufficient anticipation at a walking speed of 1 m/s. The system employs a near-infrared multi-beam laser sensor with a holographic grating generating four diamond-shaped beams, in conjunction with a high-resolution CMOS sensor. Through optical triangulation and real-time processing, the device estimates the height of obstacles or drop-offs relative to the walking surface. Vibrotactile feedback informs the user of detected hazards, with distinct vibration patterns differentiating between elevation changes and drop-offs. Preliminary trials with blind participants in controlled environments demonstrate that the system is feasible, responsive, energy-efficient, and fully compatible with conventional white cane use. Full article

A parallel-stirred tank bioreactor system on a 10 mL-scale automated with a liquid handling station introduces significant benefits in bioprocess analysis and design regarding preserving time, cost, and workload, thereby enabling quick generation of bioprocess results that can be easily scaled up. Although up-to-date approaches enable the online analysis of individual reactors for pH, dissolved oxygen (DO), and optical density (OD), the automated calibration of a new online laser-based infrared OD sensor device and noise reduction are still required. Among the extensive research on the full-data smoothing tools, the Savitzky–Golay (Savgol) filter was determined as the most effective one. Scattered and transmitted online light values were successfully aligned with the reference at-line OD values measured at 600 nm by the liquid handler with a step time of a few hours. The growth of an engineered Gluconobacter oxydans designed for specific whole-cell oxidations has been investigated in two parallel batch process setups with varied sugar types at varying sugar concentrations, combinations of sugars, and altered concentrations of complex media. Simulation of real-time smoothing was applied with a Kalman filter. Rapid adaptation was observed within a few upcoming data points by altering the parameters for the estimation of the noise in the signal. For almost all tested reaction conditions, a successful alignment of the simulation of real-time smoothed online OD with at-line values was achieved. The best growth condition was determined in the presence of 120 g L⁻¹ glucose and 30 g L⁻¹ fructose with the tripled peptone concentration. Under these conditions, OD₆₀₀ increased by 109%, from 2.1 to 4.4, compared to the reference process. Full article

The carved lacquer horse-hoof-shaped box excavated from Yulin, Shaanxi Province, represents a typical example of lacquerware preservation in the arid environment of northern China, exhibiting multiple deterioration phenomena, including substrate deformation, lacquer film peeling, and pigment fading. To systematically analyze its structural composition and craftsmanship features, this study employed multiple analytical techniques, including ultra-depth microscopy, scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), confocal laser micro-Raman spectroscopy (Raman), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Based on these analyses, a targeted conservation protocol was developed. Results revealed that the carved lacquer horse-hoof-shaped box has a wooden substrate structure, with the lacquer ash layer composed of mixed materials, including calcium carbonate (CaCO₃), quartz (SiO₂), and hydroxyapatite (Ca₁₀(PO₄)₆(OH)₂). The lacquer film layer contains Chinese lacquer and plant oils, with cinnabar applied as surface decoration. Based on these findings, a stratified reinforcement conservation strategy was proposed: under dynamic monitoring with optical fiber sensors and three-dimensional scanning, the wooden substrate was reinforced with moisture-curable polyurethane (MCPU), the lacquer ash layer was strengthened with acrylic emulsion (Primal AC33), aged areas were restored with nano calcium hydroxide (Ca(OH)₂) aqueous dispersion, and polyethylene glycol (PEG 400) poultice application was implemented to restore the flexibility of the lacquer film. This research significantly enhanced the integrity and stability of the carved lacquer horse-hoof-shaped box, providing practical evidence and technical references for the scientific conservation of lacquerware excavated from arid regions of northern China. Full article

The combination of gold nanoparticles (AuNPs) with hydrogels has drawn significant interest in the design of smart materials as advanced platforms for biomedical applications. These systems endow light-responsiveness enabled by the AuNPs localized surface plasmon resonance (LSPR) phenomenon. In this study, we propose a nanocomposite hydrogel in which gold nanorods (AuNRs) are included in an agarose–carbomer–hyaluronic acid (AC-HA)-based hydrogel matrix to study the correlation between light irradiation, local temperature increase, and drug release for potential light-assisted drug delivery applications. The gel is obtained through a facile microwave-assisted polycondensation reaction, and its properties are investigated as a function of both the hyaluronic acid molecular weight and ratio. Afterwards, AuNRs are incorporated in the AC-HA formulation, before the sol–gel transition, to impart light-responsiveness and optical properties to the otherwise inert polymeric matrix. Particular attention is given to the evaluation of AuNRs/AC-HA light-induced heat generation and drug delivery performances under near-infrared (NIR) laser irradiation in vitro. Spatiotemporal thermal profiles and high-resolution thermal maps are registered using fiber Bragg grating (FBG) sensor arrays, enabling accurate probing of maximum internal temperature variations within the composite matrix. Lastly, using a high-steric-hindrance protein (BSA) as a drug mimetic, we demonstrate that moderate localized heating under short-time repeated NIR exposure enhances the release from the nanocomposite hydrogel. Full article

This study investigates the electromagnetic field enhancement and optical response of self-assembled monolayer (SAM)-coated iron nanoparticles under laser excitation, with the aim of advancing optical gas sensing technologies. Using finite element method (FEM) simulations, we model the interaction of laser beams in both the visible (400–700 nm) and infrared (1000–2500 nm) spectral ranges with SAM-coated and uncoated nanoparticles. The results reveal that SAM coatings significantly amplify localized electromagnetic fields—reaching up to ~60 V/m in the visible range—while providing stable, wavelength-independent field distributions. In contrast, uncoated nanoparticles exhibit weaker but more variable field responses. Angular dependence analysis indicates maximal field enhancement at perpendicular (90°) detection, suggesting an orientation-sensitive design consideration for optical sensors. These findings demonstrate that SAM coatings enable stable, wavelength-independent electromagnetic responses, offering a promising pathway toward miniaturized and highly sensitive laser-based optical gas sensors. Full article

Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve significant safety risks, particularly when accessing hard-to-reach or hazardous areas. Unmanned Aerial Systems (UASs) provide a safer and more efficient alternative for surveying irregularly shaped stockpiles. This study evaluates UAS-based methods for estimating the volume of coal stockpiles at a storage facility near Cadiz, Ohio. Two sensor platforms were deployed: a Freefly Alta X quadcopter equipped with a Real-Time Kinematic (RTK) Light Detection and Ranging (LiDAR, active sensor) and a WingtraOne UAS with Post-Processed Kinematic (PPK) multispectral imaging (optical, passive sensor). Three approaches were compared: (1) LiDAR; (2) Structure-from-Motion (SfM) photogrammetry with a Digital Surface Model (DSM) and Digital Terrain Model (DTM) (SfM–DTM); and (3) an SfM-derived DSM combined with a kriging-interpolated DTM (SfM–intDTM). An automated boundary detection workflow was developed, integrating slope thresholding, Near-Infrared (NIR) spectral filtering, and Canny edge detection. Volume estimates from SfM–DTM and SfM–intDTM closely matched LiDAR-based reference estimates, with Root Mean Square Error (RMSE) values of 147.51 m³ and 146.18 m³, respectively. The SfM–intDTM approach achieved a Mean Absolute Percentage Error (MAPE) of ~2%, indicating strong agreement with LiDAR and improved accuracy compared to prior studies. A sensitivity analysis further highlighted the role of spatial resolution in volume estimation. While RMSE values remained consistent (141–162 m³) and the MAPE below 2.5% for resolutions between 0.06 m and 5 m, accuracy declined at coarser resolutions, with the MAPE rising to 11.76% at 10 m. This emphasizes the need to balance the resolution with the study objectives, geographic extent, and computational costs when selecting elevation data for volume estimation. Overall, UAS-based SfM photogrammetry combined with interpolated DTMs and automated boundary extraction offers a scalable, cost-effective, and accurate approach for stockpile volume estimation. The methodology is well-suited for both the high-precision monitoring of individual stockpiles and broader regional-scale assessments and can be readily adapted to other domains such as quarrying, agricultural storage, and forestry operations. Full article

The need for sustainable and long-term environmental monitoring has driven the development of energy-autonomous sensors, which either operate passively or integrate energy harvesting (EH) solutions. In many applications, the energy cost of data transmission is a critical factor in autonomous sensing systems. To address this challenge, optical passive sensors, which exploit changes in reflectivity to monitor physical parameters, offer self-sustained operation without requiring an external power source. Similarly, RF-based passive sensors, both chipless and with minimal circuitry, enable wireless monitoring with low power consumption. When more energy is available, EH techniques can be combined with active optical sensors. Infrared laser-based CO₂ sensors, as well as drone-mounted optical systems, demonstrate how EH can power precise environmental measurements. Beyond optics, other sensing modalities also benefit from EH, further expanding the range of self-powered environmental monitoring technologies. This review discusses the trade-offs between passive and EH-assisted sensing strategies, with a focus on optical implementations. The outlook highlights emerging solutions to enhance sensor autonomy while minimizing the energy cost of data transmission, paving the way for sustainable and scalable environmental monitoring. Full article

This work presents the recent development of a fiber-coupled multipass near-infrared (NIR) gas sensor used to monitor water vapor desorption of small material coupons. The gas sensor design employs a White cell topology to maximize the optical path length over a compact, hand-size footprint. Water vapor concentrations are quantified over a large dynamic range by simultaneously applying wavelength modulation and tunable diode laser absorption spectroscopy techniques. A custom headspace optimized for material desorption experiments is assembled using commercially available vacuum chamber components. We provide in situ measurements of water vapor desorption from two geometries of the industrially important silicone elastomer Sylgard-184 as a case study for sensor viability. To corroborate the results, the gas sensor data are compared to numerical simulations based on a triple-mode diffusion–sorption model, consisting of Henry, Langmuir, and Pooling modes. Full article

This entry describes the methods used to quantify methane emissions from either point or area sources using downwind methods. The methods described could be used as a practical guide to quantify emissions of any trace gas type from either a point or area emission source. Methane is a relatively strong greenhouse gas, its GWP is 25 times larger than CO₂ over a 100-year period, and an increase in methane anthropogenic emissions has been correlated to a changing global climate. Emission estimates that are calculated and used for national inventories are usually derived from bottom-up approaches, however there is now an increasing pressure for these to be validated by direct measurement. Calculating emission rates from downwind measurements has proven to be a versatile and relatively simple approach for direct measurement. Downwind measurement method descriptions are presented here as a practicable guide to quantifying point and area source emissions. Emission quantification is a two-stage process where methane concentration and meteorological data must be measured downwind of a source and then converted to emissions using an atmospheric dispersion model. Only four technology types currently measure in the range typical of downwind methane concentrations: metal oxide sensors, non-dispersive infrared sensors, tunable diode laser absorption spectrometers and optical cavity instruments. The choice of methane measurement is typically determined by the size of the emission source, location and the budget of the project. Meteorological data are essential to quantifying emissions, especially regarding wind speed and direction. In most cases, simple atmospheric dispersion approaches can be used to quantify both area and point emissions using these downwind measurements. Emissions can be generated using limited data (only methane concentration, wind speed, wind direction, and locations are necessary), but quantification uncertainty can be reduced using more input data. Site selection and location of instrument deployment are essential because quantification approaches assume a flat fetch (no aerodynamic obstructions) and constant wind fields. When modeling assumptions are violated, quantification uncertainty can range between +250% and −100% of the actual emission rate. At present there, is no happy medium between modeling complexity and computational time, and this remains the biggest challenge for downwind emission quantification. Full article

Quantum technologies are currently being explored for various applications, including computing, secure communication, and sensor technology. A critical aspect of achieving high-fidelity spin manipulations in quantum devices is the controlled optical initialization of electron spins. This paper introduces a low-cost programming scheme based on a 32-bit STM32F373 microcontroller, aimed at facilitating high-precision measurements of optically active solid-state spin centers within semiconductor crystals (SiC, hBN, and diamond) utilizing a multi-pulse sequence. The effective shaping of short optical pulses across semiconductor and solid-state lasers, covering the visible to near-infrared range (405–1064 nm), has been validated through photoinduced electron paramagnetic resonance (EPR) and electron nuclear double resonance (ENDOR) spectroscopies. The application of pulsed laser irradiation influences the EPR relaxation parameters associated with spin centers, which are crucial for advancements in quantum computing. The presented experimental approach facilitates the investigation of weak electron–nuclear interactions in crystals, a key factor in the development of quantum memory utilizing nuclear qubits. Full article