Search Results (237)

This paper suggests a perspective-controlled solution for an integrated Infrared micro-/nanoplastic spectroscopy/photometry-based detection, from the diffraction up to the geometry etendue, with the aim of yielding a universal spectrometer/photometer. Spectrophotometry, unlike spectroscopy that shows the interaction between matter and radiated energy, is a specific form of photometry that measures light parameters in a particular range as a function of wavelength. The solution, meant for diffraction grating and geometry etendue of the display unit, is provided by a controller that tunes the grating pitch to accommodate any emitted/transmitted wavelength from a sample made of microplastics, their degraded forms and their potential retention, and ensures that all the diffracted wavelengths are concentrated on the required etendue. The purpose is not only to go below the current Infrared limit of $20\mathsf{\mu }\mathrm{m}$ microplastic size, or to suggest an Infrared spectrophotometry geometry capable of detecting micro- and nanoplastics in the range of ($1\mathrm{nm}$–$20\mathsf{\mu }\mathrm{m}$) for integrated nano- and micro-scales, but also to transform most of the pivotal components to be directly wavelength-independent. The related controlled geometry solutions, from the controlled grating slit-width up to the controlled display unit etendue functions, are suggested for a wider generic range integration. The results from image-size characterization show that the following charge-coupled devices, nanopixel CCDs, and/or micropixel CCDs of less than $100\mathrm{nm}$ are required on the display unit, justifying the Infrared micro- and nanoplastic-integrated spectrophotometry, and the investigation conducted with other electromagnetic spectrum ranges that suggests a possible universal spectrometer/photometer. Full article

The scope of this study was to evaluate the response of Ce-doped gadolinium aluminum gallium garnet (GAGG:Ce) crystalline scintillator under medical X-ray irradiation for medical imaging applications. A 10 × 10 × 10 mm³ crystal was irradiated at X-ray tube voltages ranging from 50 kVp to 150 kVp. The crystal’s compatibility with several commercially available optical photon detectors was evaluated using the spectral matching factor (SMF) along with the absolute efficiency (AE) and the effective efficiency (EE). In addition, the energy-absorption efficiency (EAE), the quantum-detection efficiency (QDE) as well as the zero-frequency detective quantum detection efficiency DQE(0) were determined. The crystal demonstrated satisfactory AE values as high as 26.3 E.U. (where 1 E.U. = 1 μW∙m⁻²/(mR∙s⁻¹)) at 150 kVp, similar, or in some cases, even superior to other cerium-doped scintillator materials. It also exhibits adequate DQE(0) performance ranging from 0.99 to 0.95 across all the examined X-ray tube voltages. Moreover, it showed high spectral compatibility with commonly used photoreceptors in modern day such as complementary metal–oxide–semiconductors (CMOS) and charge-coupled-devices (CCD) with SMF values of 0.95 for CCD with broadband anti-reflection coating and 0.99 for hybrid CMOS blue. The aforementioned properties of this scintillator material were indicative of its superior efficiency in the examined medical energy range, compared to other commonly used scintillators. Full article

Water color remote sensing is vital for the monitoring and quantification of marine suspended sediment dynamics and their distributions. Yet validations of these observables in coastal regions and deltaic estuaries, including the Hangzhou Bay in the East China Sea, remain challenging, primarily due to the pronounced complex oceanic dynamics that exhibit high spatiotemporal variability in the signals of the suspended sediment concentration (SSC) in the ocean. Here, we integrate satellite images from the sun-synchronous satellites, China’s Huanjing (Chinese for environmental, HJ)-1A/B (charged couple device) CCD (30 m), and from Korea’s Geostationary Ocean Color Imager GOCI (500 m) to the spatiotemporal scale effects to validate SSC remote sensing-retrieved data products. A multi-scale validation framework based on coefficient of variation (CV)-based zoning was developed, where high-resolution HJ CCD SSC data were resampled to the GOCI scale (500 m), and spatial variability was quantified using CV values within corresponding HJ CCD windows. Traditional validation, comparing in situ point measurements directly with GOCI pixel-averaged data, introduces significant uncertainties due to pixel heterogeneity. The results indicate that in regions with high spatial heterogeneity (CV > 0.10), using central pixel values significantly weakens correlations and increases errors, with performance declining further in highly heterogeneous areas (CV > 0.15), underscoring the critical role of spatial averaging in mitigating scale-related biases. This study enhances the quantitative assessment of uncertainties in validating medium-to-low-resolution water color products, providing a robust approach for high-dynamic oceanic environment estuaries and bays. Full article

Automated and robotic welding have become standard practices in manufacturing, requiring precise control to maintain weld quality without relying on skilled welders. In Tungsten Inert Gas (TIG) welding, monitoring the weld pool is crucial for ensuring the necessary weld penetration, which is vital for maintaining weld integrity. Real-time observation is essential to prevent defects and improve weld quality. Various sensing technologies have been developed to address this need, with vision-based systems showing particular effectiveness in enhancing welding quality and productivity within the framework of Industry 4.0. This review looks at the latest technologies for monitoring weld pools and bead shapes. It covers methods like using Complementary Metal-Oxide Semiconductors (CMOS) to take clear images of the melt pool for better process identification, Active Appearance Model (AAM) to capture 3D images of the weld pool for accurate penetration measurement, and Charge-Coupled Devices (CCD) and Laser-Induced Breakdown Spectroscopy (LIBS) to analyze plasma spectra and create material composition graphs. Full article

Wear particle analysis, which identifies failure modes caused by the wear of various machine components, is an essential technique for monitoring machinery conditions. This analysis plays a vital role in predictive maintenance by revealing component degradation in machinery. This study proposes an automated framework to classify four standard wear particle textures—rough, striated, pitted, and fatigued—using artificial neural networks (ANNs) combined with advanced image processing techniques. Images acquired via Charged-Coupled Device (CCD) microscopy were preprocessed using sharpening, histogram stretching, and four edge detection algorithms: Sobel, Laplacian, Boie–Cox, and Canny. The Laplacian and Canny methods yielded the highest classification accuracies of 97.9% and 98.9%, respectively. By minimizing human subjectivity, this automated approach enhances diagnostic consistency and represents a scalable solution for industrial condition monitoring. Full article

In this paper, we present the analysis of functional alloy samples containing metals aluminum (Al), copper (Cu), lead (Pb), silicon (Si), tin (Sn), and zinc (Zn) using a Q-switched Nd laser operating at a wavelength of 532 nm with a pulse duration of 5 ns. Nine pelletized alloy samples were prepared, each containing varying chemical concentrations (wt.%) of Al, Cu, Pb, Si, Sn, and Zn—elements commonly used in electrotechnical and thermal functional materials. The laser beam is focused on the target surface, and the resulting emission spectrum is captured within the temperature interval of $9.0\times {10}^3$ to $1.1\times {10}^4$ K using a set of compact Avantes spectrometers. Each spectrometer is equipped with a linear charged-coupled device (CCD) array set at a 2 μs gate delay for spectrum recording. The quantitative analysis was performed using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) under the assumptions of optically thin plasma and self-absorption-free conditions, as well as local thermodynamic equilibrium (LTE). The net normalized integrated intensities of the selected emission lines were utilized for the analysis. The intensities were normalized by dividing the net integrated intensity of each line by that of the aluminum emission line (Al II) at 281.62 nm. The results obtained using CF-LIBS were compared with those from the laser ablation time-of-flight mass spectrometer (LA-TOF-MS), showing good agreement between the two techniques. Furthermore, a random forest technique (RFT) was employed using LIBS spectral data for sample classification. The RFT technique achieves the highest accuracy of ~98.89% using out-of-bag (OOB) estimation for grouping, while a 10-fold cross-validation technique, implemented for comparison, yields a mean accuracy of ~99.12%. The integrated use of LIBS, LA-TOF-MS, and machine learning (e.g., RFT) enables fast, preparation-free analysis and classification of functional metallic materials, highlighting the synergy between quantitative techniques and data-driven methods. Full article

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments. Full article

Ultrafast diagnostic technology has caused breakthroughs in fields such as inertial confinement fusion, particle accelerator research, and laser-induced phenomena. As the most widely used tool for ultrafast diagnostic technology, investigating the characteristics of streak cameras in the imaging process and streak tubes’ complex physical processes is significant for its overall development. In this work, the imaging process of a streak camera is modeled and simulated using Geant4-based Monte Carlo simulations. Based on the selected phosphor screen P43 (Gd₂O₂S: Tb) and charged coupled device (CCD) sensor parameters, Monte Carlo simulation models of phosphor screens and CCD sensors (We refer to the sensor parameters of the US company onsemi’s KAF-50100 sensor, but some adjustments are made during the simulation), implemented with the toolkit Geant4, are used to study the electron beam to generate fluorescence on phosphor and photoelectrons on CCD sensors. The physical process of a high-energy electron beam hitting a phosphor screen and imaging on the CCD camera is studied. Meanwhile, merits such as the luminous efficiency of the selected phosphor, spatial resolution of the phosphor screen, and spatial resolution of the selected CCD sensor are analyzed. The simulation results show that the phosphor screen and CCD sensor simulation models can accurately simulate the selected components’ performance parameters with the imaging process’ simulation results precisely reflecting the distribution of output electrons in the streak image tube. References for simulation and device selection in the subsequent research on streak cameras can be provided. Full article

A novel, portable chemiluminescence (CL) sensing platform powered by deep learning and smartphone integration has been developed for cost-effective and selective glucose detection. This platform features low-cost, wax-printed micro-pads (WPµ-pads) on paper-based substrates used to construct a miniaturized CL sensor. A 3D-printed black box serves as a compact WPµ-pad sensing chamber, replacing traditional bulky equipment, such as charge coupled device (CCD) cameras and optical sensors. Smartphone integration enables a seamless and user-friendly diagnostic experience, making this platform highly suitable for point-of-care (PoC) applications. Deep learning models significantly enhance the platform’s performance, offering superior accuracy and efficiency in CL image analysis. A dataset of 600 experimental CL images was utilized, out of which 80% were used for model training, with 20% of the images reserved for testing. Comparative analysis was conducted using multiple deep learning models, including Random Forest, the Support Vector Machine (SVM), InceptionV3, VGG16, and ResNet-50, to identify the optimal architecture for accurate glucose detection. The CL sensor demonstrates a linear detection range of 10–1000 µM, with a low detection limit of 8.68 µM. Extensive evaluations confirmed its stability, repeatability, and reliability under real-world conditions. This deep learning-powered platform not only improves the accuracy of analyte detection, but also democratizes access to advanced diagnostics through cost-effective and portable technology. This work paves the way for next-generation biosensing, offering transformative potential in healthcare and other domains requiring rapid and reliable analyte detection. Full article

To improve the performance of Radio Frequency Identification (RFID) multi-label systems, the multi-label network structure needs to be quickly located and optimized. A multi-label location measurement method based on the NLM–Harris algorithm is proposed in this paper. Firstly, multi-label geometric distribution images are obtained through a label image acquisition system of a multi-label semi-physical simulation platform with two vertical Charge-Coupled Device (CCD) cameras, and Gaussian noise is added to the image to simulate thermoelectric interference. Then, a fast NLM algorithm that optimizes the kernel coefficient acquisition speed is used for image denoising. Finally, the Harris corner algorithm is used to obtain the corner points of the images. After screening the diagonal points, the pixel coordinates of the preset origin and the four corners of the labels are obtained. Furthermore, the actual coordinates of the labels are obtained according to the pixel relationship. The results show that the average absolute errors of x, y, and z coordinates are 0.773 mm, 0.782 mm, and 0.807 mm, respectively. In addition, the relative errors are 1.659%, 2.260%, and 0.258%, which shows the high location accuracy of the multi-label network. It is of great significance to measure and optimize the performance of multi-label systems. Full article

Structured illumination microscopy (SIM) has attracted much attention from researchers due to its high accuracy, high efficiency, and strong adaptability. In SIM, demodulation is a key point to recovering three-dimensional topography, which directly affects the accuracy and validity of measurement. The traditional demodulation methods are the phase-shift method and Fourier transform method. The phase-shift method has a high demodulation accuracy, but its time consumption is too long. The Fourier transform method has high efficiency, but its demodulation accuracy is lower due to the loss of high frequency information during the process of filtering. However, in actual measurement, due to the gamma effect of the projector and charge-coupled device (CCD), the phase-shift interval is not strictly equal to the default value, which causes phase-shift error. Therefore, the restored topography contains carrier frequency fringes, which affects the accuracy of the measurement and limits the wide application of SIM. In this paper, a novel demodulation algorithm based on spatial-domain carrier frequency shift is proposed to solve the problem. Through recombining multiple full-period phase-shift images, the error spectrum and the signal spectrum are separated from each other in the frequency domain, so as to eliminate the effect of carrier frequency fringes. Simulations and experiments are carried out to verify the feasibility of the proposed method. Full article

In the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross–Li model in current research. However, the Ross–Li BRDF model is based on the linear relationship between the kernel models and is unable to take into account the nonlinear relationship between them. Furthermore, when using SBAF to account for spectral difference, a radiative transfer model is often used, but it requires many parameters to be set, which may introduce more errors and reduce the calibration accuracy. To address these issues, the random forest algorithm and a spectral interpolation convolution method using the Sentinel-2/multispectral instrument (MSI) are proposed in this study, in which the HuanJing-2A (HJ-2A)/charge-coupled device (CCD3) sensor is taken as an example, and the Dunhuang radiometric calibration site (DRCS) is used as a radiometric delivery platform. Firstly, a BRDF model by using the random forest algorithm of the DRCS is constructed using time-series MODIS images, which corrects the viewing geometry difference. Secondly, the BRDF correction coefficients, MSI reflectance, and relative spectral responses (RSRs) of CCD3 are used to correct the spectral differences. Finally, with the validation results, the maximum relative error between the calibration results of the proposed method and the official calibration coefficients (OCCs) published by the China Centre for Resources Satellite Data and Application (CRESDA) is 3.38%. When tested using the Baotou sandy site, the proposed method is better than the OCCs of the average relative errors calculated for all the bands except for the near-infrared (NIR) band, which has a larger error. Additionally, the effects of the light-matching method and the radiative transfer method, different approaches to constructing the BRDF model, using SBAF to account for spectral differences, different BRDF sources, as well as the imprecise viewing geometrical parameters, spectral interpolation method, and geometric positioning error, on the calibration results are analyzed. Results indicate that the cross-calibration coefficients obtained using the random forest algorithm and the proposed spectral interpolation method are more applicable to the CCD3; thus, they also account for the nonlinear relationships between the kernel models and reduce the error due to the radiative transfer model. The total uncertainty of the proposed method in all bands is less than 5.16%. Full article

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approach for the fusion of attitude data obtained from charge-coupled device (CCD) spot-positioning-based attitude measurements, differential power sensing (DPS), and differential wavefront sensing (DWS). This approach aims to obtain more robust and lower-noise-level attitude data. A system is designed based on the Michelson interferometer for link simulations; validation experiments are also conducted. The experimental results demonstrate that the fused data exhibit higher robustness. Even in the case of a single sensor failure, valid attitude data can still be obtained. Additionally, the fused data have lower noise levels, with root mean square errors of 9.5%, 37.4%, and 93.4% for the single CCD, DPS, and DWS noise errors, respectively. Full article

A low-cost laser-induced breakdown spectroscopy (LIBS) instrument equipped with a charge-coupled device (CCD) was tested in the atmospheric environment for the quantification of K, Ca, Mg, and Mn in some organo–mineral fertilizers, mineral P fertilizers, and rock fertilizers of various compositions and origins, using flame atomic absorption spectrometry (FAAS) as the reference technique. The correlation analysis performed between each CCD pixel and the corresponding element concentration measured by FAAS allowed to choose the most appropriate K, Ca, Mg and Mn emission lines for LIBS analysis. The normalization process applied to LIBS spectra to correct physical matrix effects and small fluctuations was able to increase the linear correlation of the calibration curves between LIBS data and FAAS data by an average of 0.15 points of the R-value for all elements of interest. The R values of calibration curves were 0.97, 0.96, 0.86 and 0.84, for K, Ca, Mg and Mn, respectively. The limits of detection (LOD) were 66 mg/kg (K), 35 mg/kg (Ca), 5.4 mg/kg (Mg) and 0.8 mg/kg (Mn) when using LIBS in the quantification model. The cross-validation (leave-one-out) analysis yielded an absolute average error of 12% (K), 21% (Ca), 8% (Mg) and 13% (Mn) when LIBS data were correlated to FAAS ones. These results showed that the calibration models used were close to the optimization limit and satisfactory for K, Ca, Mg, and Mn quantification in the fertilizers and rocks examined. Full article

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites. Full article